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jncraton/codet5p-220m-py-ct2-int8 | jncraton | 2023-07-08T19:12:46Z | 669 | 1 | transformers | [
"transformers",
"arxiv:2305.07922",
"license:bsd-3-clause",
"endpoints_compatible",
"region:us"
] | null | 2023-06-30T18:48:16Z | ---
license: bsd-3-clause
---
# CodeT5+ 220M (further tuned on Python)
## Model description
[CodeT5+](https://github.com/salesforce/CodeT5/tree/main/CodeT5+) is a new family of open code large language models with an encoder-decoder architecture that can flexibly operate in different modes (i.e. _encoder-only_, _decoder-only_, and _encoder-decoder_) to support a wide range of code understanding and generation tasks.
It is introduced in the paper:
[CodeT5+: Open Code Large Language Models for Code Understanding and Generation](https://arxiv.org/pdf/2305.07922.pdf)
by [Yue Wang](https://yuewang-cuhk.github.io/)\*, [Hung Le](https://sites.google.com/view/henryle2018/home?pli=1)\*, [Akhilesh Deepak Gotmare](https://akhileshgotmare.github.io/), [Nghi D.Q. Bui](https://bdqnghi.github.io/), [Junnan Li](https://sites.google.com/site/junnanlics), [Steven C.H. Hoi](https://sites.google.com/view/stevenhoi/home) (* indicates equal contribution).
Compared to the original CodeT5 family (base: `220M`, large: `770M`), CodeT5+ is pretrained with a diverse set of pretraining tasks including _span denoising_, _causal language modeling_, _contrastive learning_, and _text-code matching_ to learn rich representations from both unimodal code data and bimodal code-text data.
Additionally, it employs a simple yet effective _compute-efficient pretraining_ method to initialize the model components with frozen off-the-shelf LLMs such as [CodeGen](https://github.com/salesforce/CodeGen) to efficiently scale up the model (i.e. `2B`, `6B`, `16B`), and adopts a "shallow encoder and deep decoder" architecture.
Furthermore, it is instruction-tuned to align with natural language instructions (i.e. InstructCodeT5+ 16B) following [Code Alpaca](https://github.com/sahil280114/codealpaca).
## How to use
This model can be easily loaded using the `T5ForConditionalGeneration` functionality and employs the same tokenizer as original [CodeT5](https://github.com/salesforce/CodeT5).
```python
from transformers import T5ForConditionalGeneration, AutoTokenizer
checkpoint = "Salesforce/codet5p-220m-py"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = T5ForConditionalGeneration.from_pretrained(checkpoint).to(device)
inputs = tokenizer.encode("def print_hello_world():", return_tensors="pt").to(device)
outputs = model.generate(inputs, max_length=10)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
# ==> print('Hello World!')
```
## Pretraining data
This checkpoint is trained on the stricter permissive subset of the deduplicated version of the [github-code dataset](https://huggingface.co/datasets/codeparrot/github-code).
The data is preprocessed by reserving only permissively licensed code ("mit" “apache-2”, “bsd-3-clause”, “bsd-2-clause”, “cc0-1.0”, “unlicense”, “isc”).
Supported languages (9 in total) are as follows:
`c`, `c++`, `c-sharp`, `go`, `java`, `javascript`, `php`, `python`, `ruby.`
## Training procedure
This checkpoint is first trained on the multilingual unimodal code data at the first-stage pretraining, which includes a diverse set of pretraining tasks including _span denoising_ and two variants of _causal language modeling_.
After that, it is further trained on the Python subset with the causal language modeling objective for another epoch to better adapt for Python code generation. Please refer to the paper for more details.
## Evaluation results
CodeT5+ models have been comprehensively evaluated on a wide range of code understanding and generation tasks in various settings: _zero-shot_, _finetuning_, and _instruction-tuning_.
Specifically, CodeT5+ yields substantial performance gains on many downstream tasks compared to their SoTA baselines, e.g.,
8 text-to-code retrieval tasks (+3.2 avg. MRR), 2 line-level code completion tasks (+2.1 avg. Exact Match), and 2 retrieval-augmented code generation tasks (+5.8 avg. BLEU-4).
In 2 math programming tasks on MathQA-Python and GSM8K-Python, CodeT5+ models of below billion-parameter sizes significantly outperform many LLMs of up to 137B parameters.
Particularly, in the zero-shot text-to-code generation task on HumanEval benchmark, InstructCodeT5+ 16B sets new SoTA results of 35.0% pass@1 and 54.5% pass@10 against other open code LLMs, even surpassing the closed-source OpenAI code-cushman-001 mode
Please refer to the [paper](https://arxiv.org/pdf/2305.07922.pdf) for more details.
Specifically for this checkpoint, it achieves 12.0% pass@1 on HumanEval in the zero-shot setting, which outperforms much larger LLMs such as Incoder 1.3B’s 8.9%, GPT-Neo 2.7B's 6.4%, and GPT-J 6B's 11.6%.
## BibTeX entry and citation info
```bibtex
@article{wang2023codet5plus,
title={CodeT5+: Open Code Large Language Models for Code Understanding and Generation},
author={Wang, Yue and Le, Hung and Gotmare, Akhilesh Deepak and Bui, Nghi D.Q. and Li, Junnan and Hoi, Steven C. H.},
journal={arXiv preprint},
year={2023}
}
``` |
Word2vec/wikipedia2vec_enwiki_20180420_100d | Word2vec | 2023-07-08T19:12:30Z | 0 | 0 | null | [
"word2vec",
"en",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2023-05-16T17:00:06Z | ---
license: apache-2.0
tags:
- word2vec
datasets:
- wikipedia
language:
- en
---
## Information
Pretrained Word2vec in English. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_enwiki_20180420_100d", filename="enwiki_20180420_100d.txt"))
model.most_similar("your_word")
```
## Citation
```
@inproceedings{yamada2020wikipedia2vec,
title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
year = {2020},
publisher = {Association for Computational Linguistics},
pages = {23--30}
}
```
|
Word2vec/wikipedia2vec_enwiki_20180420_win10_100d | Word2vec | 2023-07-08T19:10:30Z | 0 | 0 | null | [
"word2vec",
"en",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2023-05-16T17:02:16Z | ---
license: apache-2.0
tags:
- word2vec
datasets:
- wikipedia
language:
- en
---
## Information
Pretrained Word2vec in English. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_enwiki_20180420_win10_100d", filename="enwiki_20180420_win10_100d.txt"))
model.most_similar("your_word")
```
## Citation
```
@inproceedings{yamada2020wikipedia2vec,
title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
year = {2020},
publisher = {Association for Computational Linguistics},
pages = {23--30}
}
```
|
Word2vec/wikipedia2vec_enwiki_20180420_nolg_300d | Word2vec | 2023-07-08T19:06:31Z | 0 | 0 | null | [
"word2vec",
"en",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2023-05-17T13:48:27Z | ---
license: apache-2.0
tags:
- word2vec
datasets:
- wikipedia
language:
- en
---
## Information
Pretrained Word2vec in English. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_enwiki_20180420_nolg_300d", filename="enwiki_20180420_nolg_300d.txt"))
model.most_similar("your_word")
```
## Citation
```
@inproceedings{yamada2020wikipedia2vec,
title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
year = {2020},
publisher = {Association for Computational Linguistics},
pages = {23--30}
}
```
|
c72599/a2c-PandaReachDense-v2 | c72599 | 2023-07-08T18:55:51Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-08T18:53:05Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.94 +/- 0.71
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
tyavika/Distil-CNN512LSTM256NoBi | tyavika | 2023-07-08T18:48:27Z | 84 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-07-02T11:03:22Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Distil-CNN512LSTM256NoBi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Distil-CNN512LSTM256NoBi
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3388
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.6009 | 1.0 | 3290 | 1.2927 |
| 1.0288 | 2.0 | 6580 | 1.1467 |
| 0.7497 | 3.0 | 9870 | 1.1902 |
| 0.5288 | 4.0 | 13160 | 1.3388 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
spitfire4794/photo | spitfire4794 | 2023-07-08T18:40:04Z | 287 | 8 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"photorealistic",
"photoreal",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-06-04T18:28:38Z | ---
language:
- en
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- photorealistic
- photoreal
- diffusers
inference: true
pipeline_tag: text-to-image
library_name: diffusers
---
# the original but with inference api enabled because why not
# Dreamlike Photoreal 2.0 is a photorealistic model based on Stable Diffusion 1.5, made by [dreamlike.art](https://dreamlike.art/).
# If you want to use dreamlike models on your website/app/etc., check the license at the bottom first!
Warning: This model is horny! Add "nude, naked" to the negative prompt if want to avoid NSFW.
You can add **photo** to your prompt to make your gens look more photorealistic.
Non-square aspect ratios work better for some prompts. If you want a portrait photo, try using a vertical aspect ratio. If you want a landscape photo, try using a horizontal aspect ratio.
This model was trained on 768x768px images, so use 768x768px, 640x896px, 896x640px, etc. It also works pretty good with higher resolutions such as 768x1024px or 1024x768px.
### Examples
<img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/preview1.jpg" style="max-width: 800px;" width="100%"/>
<img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/preview2.jpg" style="max-width: 800px;" width="100%"/>
<img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/preview3.jpg" style="max-width: 800px;" width="100%"/>
### dreamlike.art
You can use this model for free on [dreamlike.art](https://dreamlike.art/)!
<img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/dreamlike.jpg" style="max-width: 1000px;" width="100%"/>
### CKPT
[Download dreamlike-photoreal-2.0.ckpt (2.13GB)](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/dreamlike-photoreal-2.0.ckpt)
### Safetensors
[Download dreamlike-photoreal-2.0.safetensors (2.13GB)](https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/dreamlike-photoreal-2.0.safetensors)
### 🧨 Diffusers
This model can be used just like any other Stable Diffusion model. For more information,
please have a look at the [Stable Diffusion Pipeline](https://huggingface.co/docs/diffusers/api/pipelines/stable_diffusion).
```python
from diffusers import StableDiffusionPipeline
import torch
model_id = "dreamlike-art/dreamlike-photoreal-2.0"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "photo, a church in the middle of a field of crops, bright cinematic lighting, gopro, fisheye lens"
image = pipe(prompt).images[0]
image.save("./result.jpg")
```
<img src="https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/resolve/main/church.jpg" style="max-width: 640px;" width="100%"/>
# License
This model is licesed under a **modified** CreativeML OpenRAIL-M license.
- **You are not allowed to host, finetune, or do inference with the model or its derivatives on websites/apps/etc. If you want to, please email us at [email protected]**
- **You are free to host the model card and files (Without any actual inference or finetuning) on both commercial and non-commercial websites/apps/etc. Please state the full model name (Dreamlike Photoreal 2.0) and include the license as well as a link to the model card (https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0)**
- **You are free to use the outputs (images) of the model for commercial purposes in teams of 10 or less**
- You can't use the model to deliberately produce nor share illegal or harmful outputs or content
- The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
- You may re-distribute the weights. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the **modified** CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here: https://huggingface.co/dreamlike-art/dreamlike-photoreal-2.0/blob/main/LICENSE.md |
Word2vec/wikipedia2vec_plwiki_20180420_300d | Word2vec | 2023-07-08T18:36:17Z | 0 | 0 | null | [
"word2vec",
"pl",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2023-05-17T08:52:46Z | ---
license: apache-2.0
tags:
- word2vec
datasets:
- wikipedia
language:
- pl
---
## Information
Pretrained Word2vec in Polish. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_plwiki_20180420_300d", filename="plwiki_20180420_300d.txt"))
model.most_similar("your_word")
```
## Citation
```
@inproceedings{yamada2020wikipedia2vec,
title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
year = {2020},
publisher = {Association for Computational Linguistics},
pages = {23--30}
}
``` |
Word2vec/wikipedia2vec_zhwiki_20180420_100d | Word2vec | 2023-07-08T18:33:31Z | 0 | 3 | null | [
"word2vec",
"zh",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2023-05-16T17:00:34Z | ---
license: apache-2.0
tags:
- word2vec
datasets:
- wikipedia
language:
- zh
---
## Information
Pretrained Word2vec in Chinese. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_zhwiki_20180420_100d", filename="zhwiki_20180420_100d.txt"))
model.most_similar("your_word")
```
## Citation
```
@inproceedings{yamada2020wikipedia2vec,
title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
year = {2020},
publisher = {Association for Computational Linguistics},
pages = {23--30}
}
``` |
Word2vec/wikipedia2vec_jawiki_20180420_300d | Word2vec | 2023-07-08T18:31:54Z | 0 | 1 | null | [
"word2vec",
"ja",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2023-05-17T08:53:12Z | ---
license: apache-2.0
tags:
- word2vec
datasets:
- wikipedia
language:
- ja
---
## Information
Pretrained Word2vec in Japanese. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_jawiki_20180420_300d", filename="jawiki_20180420_300d.txt"))
model.most_similar("your_word")
```
## Citation
```
@inproceedings{yamada2020wikipedia2vec,
title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
year = {2020},
publisher = {Association for Computational Linguistics},
pages = {23--30}
}
```
|
Word2vec/wikipedia2vec_arwiki_20180420_100d | Word2vec | 2023-07-08T18:29:53Z | 0 | 0 | null | [
"word2vec",
"ar",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2023-05-16T16:51:26Z | ---
license: apache-2.0
tags:
- word2vec
datasets:
- wikipedia
language:
- ar
---
## Information
Pretrained Word2vec in Arabic. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_arwiki_20180420_100d", filename="arwiki_20180420_100d.txt"))
model.most_similar("your_word")
```
## Citation
```
@inproceedings{yamada2020wikipedia2vec,
title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
year = {2020},
publisher = {Association for Computational Linguistics},
pages = {23--30}
}
``` |
Word2vec/wikipedia2vec_itwiki_20180420_300d | Word2vec | 2023-07-08T18:25:14Z | 0 | 0 | null | [
"word2vec",
"it",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2023-05-17T08:53:36Z | ---
license: apache-2.0
tags:
- word2vec
datasets:
- wikipedia
language:
- it
---
## Information
Pretrained Word2vec in Italian. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_itwiki_20180420_300d", filename="itwiki_20180420_300d.txt"))
model.most_similar("your_word")
```
## Citation
```
@inproceedings{yamada2020wikipedia2vec,
title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
year = {2020},
publisher = {Association for Computational Linguistics},
pages = {23--30}
}
``` |
cagarraz/dqn-SpaceInvadersNoFrameskip-v4 | cagarraz | 2023-07-08T18:23:01Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-08T18:22:29Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 268.50 +/- 78.17
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga cagarraz -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga cagarraz -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga cagarraz
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 100000),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Mozart-coder/BERT_Dec-6_tokenized | Mozart-coder | 2023-07-08T18:20:58Z | 129 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-10-25T05:03:42Z | ---
tags:
- generated_from_trainer
model-index:
- name: BERT_Dec-6_tokenized
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# BERT_Dec-6_tokenized
This model is a fine-tuned version of [armheb/DNA_bert_6](https://huggingface.co/armheb/DNA_bert_6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0372
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0625 | 1.0 | 273 | 0.0376 |
| 0.039 | 2.0 | 546 | 0.0375 |
| 0.0385 | 3.0 | 819 | 0.0358 |
| 0.0375 | 4.0 | 1092 | 0.0380 |
| 0.0374 | 5.0 | 1365 | 0.0387 |
| 0.0358 | 6.0 | 1638 | 0.0378 |
| 0.0363 | 7.0 | 1911 | 0.0381 |
| 0.0373 | 8.0 | 2184 | 0.0377 |
| 0.0362 | 9.0 | 2457 | 0.0373 |
| 0.037 | 10.0 | 2730 | 0.0380 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Word2vec/wikipedia2vec_frwiki_20180420_300d | Word2vec | 2023-07-08T18:13:55Z | 0 | 0 | null | [
"word2vec",
"fr",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2023-05-17T09:15:49Z | ---
license: apache-2.0
tags:
- word2vec
datasets:
- wikipedia
language:
- fr
---
## Information
Pretrained Word2vec in French. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_frwiki_20180420_300d", filename="frwiki_20180420_300d.txt"))
model.most_similar("your_word")
```
## Citation
```
@inproceedings{yamada2020wikipedia2vec,
title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
year = {2020},
publisher = {Association for Computational Linguistics},
pages = {23--30}
}
```
|
Word2vec/wikipedia2vec_dewiki_20180420_300d | Word2vec | 2023-07-08T18:13:11Z | 0 | 0 | null | [
"word2vec",
"de",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2023-05-17T09:07:09Z | ---
license: apache-2.0
tags:
- word2vec
datasets:
- wikipedia
language:
- de
---
## Information
Pretrained Word2vec in German. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_dewiki_20180420_300d", filename="dewiki_20180420_300d.txt"))
model.most_similar("your_word")
```
## Citation
```
@inproceedings{yamada2020wikipedia2vec,
title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
year = {2020},
publisher = {Association for Computational Linguistics},
pages = {23--30}
}
```
|
TomyAI/Slider | TomyAI | 2023-07-08T18:05:08Z | 0 | 9 | null | [
"ja",
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-08T17:49:31Z | ---
language:
- ja
thumbnail: NamedDiapers_1.png
license: creativeml-openrail-m
---
乳首の色、サイズ、おっぱいの寄り具合、高さを調整するスライダーLoRAです。
個別にダウンロードするかOppaiSliderPack.zipをダウンロードして解凍してください。
 |
skrl/IsaacGymEnvs-Ant-PPO | skrl | 2023-07-08T18:04:35Z | 0 | 0 | skrl | [
"skrl",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-24T21:15:29Z | ---
library_name: skrl
tags:
- deep-reinforcement-learning
- reinforcement-learning
- skrl
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 5094.76 +/- 310.06
name: Total reward (mean)
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: IsaacGymEnvs-Ant
type: IsaacGymEnvs-Ant
---
<!-- ---
torch: 4996.72 +/- 273.16
jax: 5094.76 +/- 310.06
numpy: 4542.73 +/- 467.69
--- -->
# IsaacGymEnvs-Ant-PPO
Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments.
- **Task:** Ant
- **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html)
# Usage (with skrl)
Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts.
* PyTorch
```python
from skrl.utils.huggingface import download_model_from_huggingface
# assuming that there is an agent named `agent`
path = download_model_from_huggingface("skrl/IsaacGymEnvs-Ant-PPO", filename="agent.pt")
agent.load(path)
```
* JAX
```python
from skrl.utils.huggingface import download_model_from_huggingface
# assuming that there is an agent named `agent`
path = download_model_from_huggingface("skrl/IsaacGymEnvs-Ant-PPO", filename="agent.pickle")
agent.load(path)
```
# Hyperparameters
Note: Undefined parameters keep their values by default.
```python
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 4
cfg["mini_batches"] = 2 # 16 * 4096 / 32768
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 1.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.01
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
```
|
Word2vec/wikipedia2vec_eswiki_20180420_300d | Word2vec | 2023-07-08T17:58:18Z | 0 | 1 | null | [
"word2vec",
"es",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2023-05-17T08:53:59Z | ---
license: apache-2.0
tags:
- word2vec
datasets:
- wikipedia
language:
- es
---
## Information
Pretrained Word2vec in Spanish. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_eswiki_20180420_300d", filename="eswiki_20180420_300d.txt"))
model.most_similar("your_word")
```
## Citation
```
@inproceedings{yamada2020wikipedia2vec,
title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
year = {2020},
publisher = {Association for Computational Linguistics},
pages = {23--30}
}
``` |
Word2vec/wikipedia2vec_ptwiki_20180420_100d | Word2vec | 2023-07-08T17:57:30Z | 0 | 0 | null | [
"word2vec",
"pt",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2023-05-16T17:00:56Z | ---
license: apache-2.0
tags:
- word2vec
datasets:
- wikipedia
language:
- pt
---
## Information
Pretrained Word2vec in Portuguese. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_ptwiki_20180420_100d", filename="ptwiki_20180420_100d.txt"))
model.most_similar("your_word")
```
## Citation
```
@inproceedings{yamada2020wikipedia2vec,
title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
year = {2020},
publisher = {Association for Computational Linguistics},
pages = {23--30}
}
```
|
Word2vec/wikipedia2vec_ruwiki_20180420_100d | Word2vec | 2023-07-08T17:51:41Z | 0 | 0 | null | [
"word2vec",
"ru",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2023-05-16T17:00:45Z | ---
license: apache-2.0
tags:
- word2vec
datasets:
- wikipedia
language:
- ru
---
## Information
Pretrained Word2vec in Russian. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_ruwiki_20180420_100d", filename="ruwiki_20180420_100d.txt"))
model.most_similar("your_word")
```
## Citation
```
@inproceedings{yamada2020wikipedia2vec,
title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
year = {2020},
publisher = {Association for Computational Linguistics},
pages = {23--30}
}
``` |
Word2vec/wikipedia2vec_eswiki_20180420_100d | Word2vec | 2023-07-08T17:48:37Z | 0 | 1 | null | [
"word2vec",
"es",
"dataset:wikipedia",
"license:apache-2.0",
"region:us"
] | null | 2023-05-16T17:02:04Z | ---
license: apache-2.0
tags:
- word2vec
datasets:
- wikipedia
language:
- es
---
## Information
Pretrained Word2vec in Spanish. For more information, see [https://wikipedia2vec.github.io/wikipedia2vec/pretrained/](https://wikipedia2vec.github.io/wikipedia2vec/pretrained/).
## How to use?
```
from gensim.models import KeyedVectors
from huggingface_hub import hf_hub_download
model = KeyedVectors.load_word2vec_format(hf_hub_download(repo_id="Word2vec/wikipedia2vec_eswiki_20180420_100d", filename="eswiki_20180420_100d.txt"))
model.most_similar("your_word")
```
## Citation
```
@inproceedings{yamada2020wikipedia2vec,
title = "{W}ikipedia2{V}ec: An Efficient Toolkit for Learning and Visualizing the Embeddings of Words and Entities from {W}ikipedia",
author={Yamada, Ikuya and Asai, Akari and Sakuma, Jin and Shindo, Hiroyuki and Takeda, Hideaki and Takefuji, Yoshiyasu and Matsumoto, Yuji},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations},
year = {2020},
publisher = {Association for Computational Linguistics},
pages = {23--30}
}
``` |
IDEA-CCNL/Erlangshen-ZEN2-668M-Chinese | IDEA-CCNL | 2023-07-08T17:48:08Z | 58 | 0 | transformers | [
"transformers",
"pytorch",
"ZEN",
"chinese",
"zh",
"arxiv:2105.01279",
"arxiv:2209.02970",
"license:apache-2.0",
"region:us"
] | null | 2022-07-27T09:28:55Z | ---
language:
- zh
license: apache-2.0
tags:
- ZEN
- chinese
inference: false
---
# Erlangshen-ZEN2-668M-Chinese
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
## 简介 Brief Introduction
善于处理NLU任务，使用了N-gram编码增强文本语义，6.68亿参数量的ZEN2
ZEN2 model, which uses N-gram to enhance text semantic and has 668M parameters, is adept at NLU tasks.
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 通用 General | 自然语言理解 NLU | 二郎神 Erlangshen | ZEN2 | 668M | 中文-Chinese |
## 模型信息 Model Information
我们与[ZEN团队](https://github.com/sinovation/ZEN2)合作，使用我们的封神框架，开源发布了ZEN2模型。具体而言，通过引入无监督学习中提取的知识，ZEN通过N-gram方法学习不同的文本粒度信息。ZEN2使用大规模数据集和特殊的预训练策略对N-gram增强编码器进行预训练。下一步，我们将继续与ZEN团队一起探索PLM的优化，并提高下游任务的性能。
We open source and publicly release ZEN2 using our Fengshen Framework in collaboration with the [ZEN team](https://github.com/sinovation/ZEN2). More precisely, by bringing together knowledge extracted by unsupervised learning, ZEN learns different textual granularity information through N-gram methods. ZEN2 pre-trains the N-gram-enhanced encoders with large-scale datasets and special pre-training strategies. In the next step, we continue with the ZEN team to explore the optimization of PLM and improve the performance on downstream tasks.
### 下游效果 Performance
**分类任务 Classification**
| Model(Acc) | afqmc | tnews | iflytek | ocnli | cmnli |
| :--------: | :-----: | :----: | :-----: | :----: | :----: |
| Erlangshen-ZEN2-345M-Chinese | 0.741 | 0.584 | 0.599 | 0.788 | 0.80 |
| Erlangshen-ZEN2-668M-Chinese | 0.75 | 0.60 | 0.589 | 0.81 | 0.82 |
**抽取任务 Extraction**
| Model(F1) | WEIBO(test) | Resume(test) | MSRA(test) | OntoNote4.0(test) | CMeEE(dev) | CLUENER(dev) |
| :--------: | :-----: | :----: | :-----: | :----: | :----: | :----: |
| Erlangshen-ZEN2-345M-Chinese | 65.26 | 96.03 | 95.15 | 78.93 | 62.81 | 79.27 |
| Erlangshen-ZEN2-668M-Chinese | 70.02 | 96.08 | 95.13 | 80.89 | 63.37 | 79.22 |
## 使用 Usage
因为[transformers](https://github.com/huggingface/transformers)库中是没有ZEN2相关的模型结构的，所以你可以在我们的[Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)中找到并且运行代码。
Since there is no structure of ZEN2 in [transformers library](https://github.com/huggingface/transformers), you can find the structure of ZEN2 and run the codes in [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM).
```shell
git clone https://github.com/IDEA-CCNL/Fengshenbang-LM.git
```
```python
from fengshen.models.zen2.ngram_utils import ZenNgramDict
from fengshen.models.zen2.tokenization import BertTokenizer
from fengshen.models.zen2.modeling import ZenForSequenceClassification, ZenForTokenClassification
pretrain_path = 'IDEA-CCNL/Erlangshen-ZEN2-668M-Chinese'
tokenizer = BertTokenizer.from_pretrained(pretrain_path)
model_classification = ZenForSequenceClassification.from_pretrained(pretrain_path)
model_extraction = ZenForTokenClassification.from_pretrained(pretrain_path)
ngram_dict = ZenNgramDict.from_pretrained(pretrain_path, tokenizer=tokenizer)
```
你可以从下方的链接获得我们做分类和抽取的详细示例。
You can get classification and extraction examples below.
[分类 classification example on fengshen](https://github.com/IDEA-CCNL/Fengshenbang-LM/blob/main/fengshen/examples/zen2_finetune/fs_zen2_large_tnews.sh)
[抽取 extraction example on fengshen](https://github.com/IDEA-CCNL/Fengshenbang-LM/blob/main/fengshen/examples/zen2_finetune/ner_zen2_large_ontonotes4.sh)
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的对该模型的论文：
If you are using the resource for your work, please cite the our paper for this model:
```text
@article{Sinovation2021ZEN2,
title="{ZEN 2.0: Continue Training and Adaption for N-gram Enhanced Text Encoders}",
author={Yan Song, Tong Zhang, Yonggang Wang, Kai-Fu Lee},
journal={arXiv preprint arXiv:2105.01279},
year={2021},
}
```
如果您在您的工作中使用了我们的模型，也可以引用我们的[总论文](https://arxiv.org/abs/2209.02970)：
If you are using the resource for your work, please cite the our [overview paper](https://arxiv.org/abs/2209.02970):
```text
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
也可以引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
``` |
devan666dewa/test | devan666dewa | 2023-07-08T17:36:45Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-08T16:43:39Z | ---
license: creativeml-openrail-m
---
|
skrl/IsaacGymEnvs-BallBalance-PPO | skrl | 2023-07-08T17:29:56Z | 0 | 0 | skrl | [
"skrl",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-24T20:42:20Z | ---
library_name: skrl
tags:
- deep-reinforcement-learning
- reinforcement-learning
- skrl
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 298.89 +/- 27.4
name: Total reward (mean)
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: IsaacGymEnvs-BallBalance
type: IsaacGymEnvs-BallBalance
---
<!-- ---
torch: 298.89 +/- 27.4
jax: 256.32 +/- 12.84
numpy: 240.59 +/- 19.15
--- -->
# IsaacGymEnvs-BallBalance-PPO
Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments.
- **Task:** BallBalance
- **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html)
# Usage (with skrl)
Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts.
* PyTorch
```python
from skrl.utils.huggingface import download_model_from_huggingface
# assuming that there is an agent named `agent`
path = download_model_from_huggingface("skrl/IsaacGymEnvs-BallBalance-PPO", filename="agent.pt")
agent.load(path)
```
* JAX
```python
from skrl.utils.huggingface import download_model_from_huggingface
# assuming that there is an agent named `agent`
path = download_model_from_huggingface("skrl/IsaacGymEnvs-BallBalance-PPO", filename="agent.pickle")
agent.load(path)
```
# Hyperparameters
Note: Undefined parameters keep their values by default.
```python
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 8 # 16 * 4096 / 8192
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.1
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
```
|
mystt/llama-tr | mystt | 2023-07-08T17:10:28Z | 1 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-08T17:10:24Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.4.0.dev0
|
mark-oppenheim/rl-course-unit1-lunar-lander | mark-oppenheim | 2023-07-08T17:09:16Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-08T17:08:59Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 268.14 +/- 23.29
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
skrl/IsaacGymEnvs-Cartpole-PPO | skrl | 2023-07-08T17:03:59Z | 0 | 0 | skrl | [
"skrl",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-01-24T20:42:39Z | ---
library_name: skrl
tags:
- deep-reinforcement-learning
- reinforcement-learning
- skrl
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 493.73 +/- 0.58
name: Total reward (mean)
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: IsaacGymEnvs-Cartpole
type: IsaacGymEnvs-Cartpole
---
<!-- ---
torch: 493.73 +/- 0.58
jax: 492.06 +/- 3.58
numpy: 491.92 +/- 0.57
--- -->
# IsaacGymEnvs-Cartpole-PPO
Trained agent for [NVIDIA Isaac Gym Preview](https://github.com/NVIDIA-Omniverse/IsaacGymEnvs) environments.
- **Task:** Cartpole
- **Agent:** [PPO](https://skrl.readthedocs.io/en/latest/api/agents/ppo.html)
# Usage (with skrl)
Note: Visit the skrl [Examples](https://skrl.readthedocs.io/en/latest/intro/examples.html) section to access the scripts.
* PyTorch
```python
from skrl.utils.huggingface import download_model_from_huggingface
# assuming that there is an agent named `agent`
path = download_model_from_huggingface("skrl/IsaacGymEnvs-Cartpole-PPO", filename="agent.pt")
agent.load(path)
```
* JAX
```python
from skrl.utils.huggingface import download_model_from_huggingface
# assuming that there is an agent named `agent`
path = download_model_from_huggingface("skrl/IsaacGymEnvs-Cartpole-PPO", filename="agent.pickle")
agent.load(path)
```
# Hyperparameters
Note: Undefined parameters keep their values by default.
```python
# https://skrl.readthedocs.io/en/latest/api/agents/ppo.html#configuration-and-hyperparameters
cfg = PPO_DEFAULT_CONFIG.copy()
cfg["rollouts"] = 16 # memory_size
cfg["learning_epochs"] = 8
cfg["mini_batches"] = 1 # 16 * 512 / 8192
cfg["discount_factor"] = 0.99
cfg["lambda"] = 0.95
cfg["learning_rate"] = 3e-4
cfg["learning_rate_scheduler"] = KLAdaptiveRL
cfg["learning_rate_scheduler_kwargs"] = {"kl_threshold": 0.008}
cfg["random_timesteps"] = 0
cfg["learning_starts"] = 0
cfg["grad_norm_clip"] = 1.0
cfg["ratio_clip"] = 0.2
cfg["value_clip"] = 0.2
cfg["clip_predicted_values"] = True
cfg["entropy_loss_scale"] = 0.0
cfg["value_loss_scale"] = 2.0
cfg["kl_threshold"] = 0
cfg["rewards_shaper"] = lambda rewards, timestep, timesteps: rewards * 0.1
cfg["state_preprocessor"] = RunningStandardScaler
cfg["state_preprocessor_kwargs"] = {"size": env.observation_space, "device": device}
cfg["value_preprocessor"] = RunningStandardScaler
cfg["value_preprocessor_kwargs"] = {"size": 1, "device": device}
```
|
jason1i/whisper-small-dv | jason1i | 2023-07-08T16:31:01Z | 77 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dv",
"dataset:mozilla-foundation/common_voice_13_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-07-08T15:56:40Z | ---
language:
- dv
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Whisper Small Dv
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: dv
split: test
args: dv
metrics:
- name: Wer
type: wer
value: 13.198525576381403
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Dv
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1712
- Wer Ortho: 62.2885
- Wer: 13.1985
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.1227 | 1.63 | 500 | 0.1712 | 62.2885 | 13.1985 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
camus-ng/dreambooth_lora_cory_v15 | camus-ng | 2023-07-08T16:24:49Z | 1 | 0 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2023-07-08T12:58:54Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: a photo of <ntvc> man
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - camus-ng/dreambooth_lora_cory_v15
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on a photo of <ntvc> man using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
HeshamMamdouh/mt5-small-v8-sum-fine-tuned | HeshamMamdouh | 2023-07-08T16:10:08Z | 61 | 0 | transformers | [
"transformers",
"tf",
"mbart",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-07-08T16:09:50Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: mt5-small-v8-sum-fine-tuned
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mt5-small-v8-sum-fine-tuned
This model is a fine-tuned version of [abdalrahmanshahrour/AraBART-summ](https://huggingface.co/abdalrahmanshahrour/AraBART-summ) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.6114
- Epoch: 29
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 2.4132 | 0 |
| 2.4084 | 1 |
| 2.3948 | 2 |
| 2.3715 | 3 |
| 2.3110 | 4 |
| 2.2797 | 5 |
| 2.2699 | 6 |
| 2.2320 | 7 |
| 2.1178 | 8 |
| 2.1323 | 9 |
| 2.0598 | 10 |
| 2.1086 | 11 |
| 2.0487 | 12 |
| 1.9561 | 13 |
| 2.0067 | 14 |
| 1.8801 | 15 |
| 1.9177 | 16 |
| 1.9594 | 17 |
| 1.8946 | 18 |
| 1.8057 | 19 |
| 1.7603 | 20 |
| 1.7916 | 21 |
| 1.7090 | 22 |
| 1.7502 | 23 |
| 1.7377 | 24 |
| 1.7007 | 25 |
| 1.6427 | 26 |
| 1.6352 | 27 |
| 1.6317 | 28 |
| 1.6114 | 29 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.13.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
agercas/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan | agercas | 2023-07-08T15:42:55Z | 161 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:bsd-3-clause",
"endpoints_compatible",
"region:us"
] | audio-classification | 2023-07-08T14:53:43Z | ---
license: bsd-3-clause
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3847
- Accuracy: 0.91
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1646 | 1.0 | 225 | 0.7426 | 0.74 |
| 0.6839 | 2.0 | 450 | 0.7655 | 0.78 |
| 0.4179 | 3.0 | 675 | 0.8210 | 0.81 |
| 0.1836 | 4.0 | 900 | 0.3845 | 0.86 |
| 0.0018 | 5.0 | 1125 | 0.4368 | 0.87 |
| 0.0032 | 6.0 | 1350 | 0.4066 | 0.9 |
| 0.0001 | 7.0 | 1575 | 0.4524 | 0.89 |
| 0.0 | 8.0 | 1800 | 0.3708 | 0.9 |
| 0.0 | 9.0 | 2025 | 0.3975 | 0.9 |
| 0.0001 | 10.0 | 2250 | 0.3847 | 0.91 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
choozmo/choozmomic | choozmo | 2023-07-08T15:38:56Z | 2 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2023-07-08T15:38:52Z | ---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: choozmomic
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - choozmomic
These are LoRA adaption weights for [runwayml/stable-diffusion-v1-5](https://huggingface.co/runwayml/stable-diffusion-v1-5). The weights were trained on the instance prompt "choozmomic" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
|
tanmoy16/test_model | tanmoy16 | 2023-07-08T15:20:01Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-08T15:19:53Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0.dev0
|
Ocelotr/speecht5tts | Ocelotr | 2023-07-08T15:13:15Z | 94 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"ara",
"generated_from_trainer",
"ar",
"dataset:SDA_CLEAN_NAJDI",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2023-07-08T13:55:16Z | ---
language:
- ar
license: mit
tags:
- ara
- generated_from_trainer
datasets:
- SDA_CLEAN_NAJDI
model-index:
- name: SpeechT5 TTS
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SpeechT5 TTS
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the SDA dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5062
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5789 | 1.49 | 1000 | 0.5299 |
| 0.5448 | 2.97 | 2000 | 0.5150 |
| 0.5422 | 4.46 | 3000 | 0.5090 |
| 0.5417 | 5.95 | 4000 | 0.5062 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.13.0
- Tokenizers 0.13.3
|
AACEE/textual_inversion_airship | AACEE | 2023-07-08T15:03:05Z | 42 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:stabilityai/stable-diffusion-2-depth",
"base_model:adapter:stabilityai/stable-diffusion-2-depth",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-07-08T11:45:59Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-depth
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
---
# Textual inversion text2image fine-tuning - AACEE/textual_inversion_airship
These are textual inversion adaption weights for stabilityai/stable-diffusion-2-depth. You can find some example images in the following.
|
manueltonneau/biocovid-bert-large-cased | manueltonneau | 2023-07-08T15:01:09Z | 163 | 1 | transformers | [
"transformers",
"pytorch",
"en",
"doi:10.57967/hf/0869",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z | ---
language:
- en
---
All information can be found here: https://github.com/manueltonneau/covid-berts
If you find this model useful, please cite:
```
@misc {manuel_tonneau_2023,
author = { {Manuel Tonneau} },
title = { biocovid-bert-large-cased (Revision 1549866) },
year = 2023,
url = { https://huggingface.co/manueltonneau/biocovid-bert-large-cased },
doi = { 10.57967/hf/0869 },
publisher = { Hugging Face }
}
``` |
manueltonneau/clinicalcovid-bert-nli | manueltonneau | 2023-07-08T14:59:52Z | 8 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"en",
"doi:10.57967/hf/0868",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:05Z | ---
language:
- en
---
All information can be found here: https://github.com/manueltonneau/covid-berts
If you find this model useful, please cite:
```
@misc {manuel_tonneau_2023,
author = { {Manuel Tonneau} },
title = { clinicalcovid-bert-nli (Revision 9a0bad1) },
year = 2023,
url = { https://huggingface.co/manueltonneau/clinicalcovid-bert-nli },
doi = { 10.57967/hf/0868 },
publisher = { Hugging Face }
}
```
|
Quacktab/ppo-LunarLander-v2 | Quacktab | 2023-07-08T14:52:36Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-08T06:22:12Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 273.28 +/- 19.40
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
RogerB/afriberta_small-finetuned-kintweetsC | RogerB | 2023-07-08T14:50:21Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-07-08T14:41:18Z | ---
tags:
- generated_from_trainer
model-index:
- name: afriberta_small-finetuned-kintweetsC
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# afriberta_small-finetuned-kintweetsC
This model is a fine-tuned version of [castorini/afriberta_small](https://huggingface.co/castorini/afriberta_small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1630
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.5819 | 1.0 | 900 | 4.2983 |
| 4.3316 | 2.0 | 1800 | 4.1280 |
| 4.2441 | 3.0 | 2700 | 4.2305 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
C-guy/Model-V | C-guy | 2023-07-08T14:49:33Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-07T17:00:25Z | ---
license: creativeml-openrail-m
---
|
RogerB/afriberta_base-finetuned-kintweetsC | RogerB | 2023-07-08T14:40:28Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-07-08T14:28:03Z | ---
tags:
- generated_from_trainer
model-index:
- name: afriberta_base-finetuned-kintweetsC
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# afriberta_base-finetuned-kintweetsC
This model is a fine-tuned version of [castorini/afriberta_base](https://huggingface.co/castorini/afriberta_base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9927
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.4096 | 1.0 | 900 | 4.1336 |
| 4.1389 | 2.0 | 1800 | 3.9637 |
| 4.0421 | 3.0 | 2700 | 4.0400 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
bpw1621/ppo-Huggy | bpw1621 | 2023-07-08T14:10:30Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2023-07-08T14:10:20Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: bpw1621/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
agercas/distilhubert-finetuned-gtzan | agercas | 2023-07-08T14:05:22Z | 161 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | 2023-07-08T12:00:27Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5815
- Accuracy: 0.83
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3276 | 1.0 | 113 | 1.4307 | 0.53 |
| 1.0346 | 2.0 | 226 | 1.0529 | 0.68 |
| 0.92 | 3.0 | 339 | 0.7562 | 0.78 |
| 0.5665 | 4.0 | 452 | 0.6476 | 0.82 |
| 0.3491 | 5.0 | 565 | 0.5733 | 0.83 |
| 0.1923 | 6.0 | 678 | 0.4754 | 0.88 |
| 0.1646 | 7.0 | 791 | 0.5382 | 0.83 |
| 0.141 | 8.0 | 904 | 0.5623 | 0.84 |
| 0.0624 | 9.0 | 1017 | 0.5773 | 0.84 |
| 0.0706 | 10.0 | 1130 | 0.5815 | 0.83 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
openchat/openchat_v2_w | openchat | 2023-07-08T13:59:20Z | 1,484 | 30 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-07T15:31:49Z | ---
language:
- en
tags:
- llama
license: other
---
# OpenChat: Advancing Open-source Language Models with Imperfect Data
The OpenChat v2 family is inspired by offline reinforcement learning, including conditional behavior cloning (OpenChat-v2) and weighted behavior cloning (OpenChat-v2-w).
- **[OpenChat-v2-w](https://huggingface.co/openchat/openchat_v2_w)**: ~80k cleaned ShareGPT data with conditioning and weighted loss, based on LLaMA-13B with a context length of 2048.
- Achieves **50.9%** win-rate over ChatGPT on MT-bench.
- Achieves **79.4%** win-rate over ChatGPT on Vicuna-bench.
- Achieves **87.1%** win-rate over text-davinci-003 on AlpacaEval.
- **[OpenChat-v2](https://huggingface.co/openchat/openchat_v2)**: ~80k cleaned ShareGPT data with only conditioning, based on LLaMA-13B with a context length of 2048.
- Achieves **48.1%** win-rate over ChatGPT on MT-bench.
- Achieves **80.6%** win-rate over ChatGPT on Vicuna-bench.
- Achieves **85.0%** win-rate over text-davinci-003 on AlpacaEval.
## Code and Inference Server
We provide the full source code, including an inference server compatible with the "ChatCompletions" API, in the [OpenChat](https://github.com/imoneoi/openchat) GitHub repository.
## Web UI
OpenChat also includes a web UI for a better user experience. See the GitHub repository for instructions.
## Conversation Template
The conversation template **involves concatenating tokens**, and cannot be expressed in plain-text.
Besides base model vocabulary, an end-of-turn token `<|end_of_turn|>` is added.
Here is an example of single-round conversation template:
```python
def tokenize_single_input(tokenizer, prompt):
# OpenChat V2
human_prefix = "User:"
prefix = "Assistant GPT4:"
eot_token = "<|end_of_turn|>"
bos_token = "<s>"
def _tokenize(text):
return tokenizer.convert_tokens_to_ids(tokenizer._tokenize(text))
def _tokenize_special(special_name):
return tokenizer.convert_tokens_to_ids(special_name)
return [_tokenize_special(bos_token)] + _tokenize(human_prefix) + _tokenize(prompt) + [_tokenize_special(eot_token)] + \
_tokenize(prefix)
```
To explore conditional language models, you can also set `prefix = "Assistant GPT3:"` to mimic ChatGPT behavior (this may cause performance degradation).
*Hint: In BPE, `tokenize(A) + tokenize(B)` does not always equals to `tokenize(A + B)`*
## Limitations
**Foundation Model Limitations**
Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as:
- Complex reasoning
- Mathematical and arithmetic tasks
- Programming and coding challenges
**Hallucination of Non-existent Information**
OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model.
|
openchat/openchat_v2 | openchat | 2023-07-08T13:51:04Z | 1,485 | 12 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-07T15:30:54Z | ---
language:
- en
tags:
- llama
license: other
---
# OpenChat: Advancing Open-source Language Models with Imperfect Data
The OpenChat v2 family is inspired by offline reinforcement learning, including conditional behavior cloning (OpenChat-v2) and weighted behavior cloning (OpenChat-v2-w).
- **[OpenChat-v2-w](https://huggingface.co/openchat/openchat_v2_w)**: ~80k cleaned ShareGPT data with conditioning and weighted loss, based on LLaMA-13B with a context length of 2048.
- Achieves **50.9%** win-rate over ChatGPT on MT-bench.
- Achieves **79.4%** win-rate over ChatGPT on Vicuna-bench.
- Achieves **87.1%** win-rate over text-davinci-003 on AlpacaEval.
- **[OpenChat-v2](https://huggingface.co/openchat/openchat_v2)**: ~80k cleaned ShareGPT data with only conditioning, based on LLaMA-13B with a context length of 2048.
- Achieves **48.1%** win-rate over ChatGPT on MT-bench.
- Achieves **80.6%** win-rate over ChatGPT on Vicuna-bench.
- Achieves **85.0%** win-rate over text-davinci-003 on AlpacaEval.
## Code and Inference Server
We provide the full source code, including an inference server compatible with the "ChatCompletions" API, in the [OpenChat](https://github.com/imoneoi/openchat) GitHub repository.
## Web UI
OpenChat also includes a web UI for a better user experience. See the GitHub repository for instructions.
## Conversation Template
The conversation template **involves concatenating tokens**, and cannot be expressed in plain-text.
Besides base model vocabulary, an end-of-turn token `<|end_of_turn|>` is added.
Here is an example of single-round conversation template:
```python
def tokenize_single_input(tokenizer, prompt):
# OpenChat V2
human_prefix = "User:"
prefix = "Assistant GPT4:"
eot_token = "<|end_of_turn|>"
bos_token = "<s>"
def _tokenize(text):
return tokenizer.convert_tokens_to_ids(tokenizer._tokenize(text))
def _tokenize_special(special_name):
return tokenizer.convert_tokens_to_ids(special_name)
return [_tokenize_special(bos_token)] + _tokenize(human_prefix) + _tokenize(prompt) + [_tokenize_special(eot_token)] + \
_tokenize(prefix)
```
To explore conditional language models, you can also set `prefix = "Assistant GPT3:"` to mimic ChatGPT behavior (this may cause performance degradation).
*Hint: In BPE, `tokenize(A) + tokenize(B)` does not always equals to `tokenize(A + B)`*
## Limitations
**Foundation Model Limitations**
Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as:
- Complex reasoning
- Mathematical and arithmetic tasks
- Programming and coding challenges
**Hallucination of Non-existent Information**
OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model.
|
Cookieszz/Xiaoz | Cookieszz | 2023-07-08T13:39:19Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-07-08T13:39:19Z | ---
license: bigscience-openrail-m
---
|
hopkins/eng-kor-union | hopkins | 2023-07-08T13:28:48Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | 2023-07-08T13:11:30Z | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-kor-union
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eng-kor-union
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9652
- Bleu: 7.5454
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
swl-models/NullStyle-v2.0 | swl-models | 2023-07-08T13:08:31Z | 0 | 1 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-08T13:05:14Z | ---
license: creativeml-openrail-m
---
|
Falah/stable_diffusion_prompts_gen | Falah | 2023-07-08T13:05:30Z | 21 | 3 | diffusers | [
"diffusers",
"pytorch",
"gpt2",
"art",
"stable diffusion",
"text-generation",
"en",
"dataset:Falah/stable_diffusion_prompts_dataset",
"license:apache-2.0",
"region:us"
] | text-generation | 2023-07-08T09:30:49Z | ---
license: apache-2.0
datasets:
- Falah/stable_diffusion_prompts_dataset
language:
- en
metrics:
- accuracy
library_name: diffusers
pipeline_tag: text-generation
tags:
- art
- stable diffusion
- gpt2
---
# Stable Diffusion Prompts Generation Model
This model is designed for generating illustration art style prompts for the Stable Diffusion tool for text-to-image generation.
It utilizes the custom dataset "Falah/stable_diffusion_prompts_dataset" to generate creative and coherent text prompts.
## Examples
To load the model and generate inferences using the model, you can use the following code snippet:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "Falah/stable_diffusion_prompts_gen"
dataset_name = "Falah/stable_diffusion_prompts_dataset"
prompt = r'a beautiful female' # the beginning of the prompt
temperature = 0.9 # A higher temperature will produce more diverse results, but with a higher risk of less coherent text
top_k = 8 # the number of tokens to sample from at each step
max_length = 200 # the maximum number of tokens for the output of the model
repetition_penalty = 1.2 # the penalty value for each repetition of a token
num_return_sequences = 5 # the number of results to generate
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
input_ids = tokenizer(prompt, return_tensors='pt').input_ids
output = model.generate(
input_ids,
do_sample=True,
temperature=temperature,
top_k=top_k,
max_length=max_length,
num_return_sequences=num_return_sequences,
repetition_penalty=repetition_penalty,
early_stopping=True
)
print('\033[96m' + prompt + '\033[0m')
for i in range(len(output)):
print(tokenizer.decode(output[i], skip_special_tokens=True) + '\n')
```
These are examples of prompts generating and testing the model with the website [STABLE DIFFUSION XL](https://clipdrop.co/) for the stable diffusion model
generating images from prompts
```
a beautiful female
a beautiful female woman, and she's got the best hair in this world. I'm not saying her look is bad (I think it has to be), but my point was that when one looks at these things like we're all looking for something different about our bodies as individuals they are completely wrong; there isn't anything inherently evil with being an animal or having two legs instead of just walking on both sides of you while holding your other leg up so tightly around yourself - no matter how
```
## another generating prompts
```
a beautiful female and she's been in the business for over 30 years.
I've had my fair share of bad things, and I'm sure many more will befall me at some point as well… but it is one thing when you have such an incredible woman on your team that makes life so difficult to bear (aside from being very much human) while also having her back with no regard whatsoever towards any personal issues or even just trying desperately hard not too far away! And
```
--------------
Feel free to modify the parameters like `prompt`, `temperature`, `top_k`, etc., to experiment with different outputs.
## Citation
If you use this model or the associated dataset in your research or projects, please cite it as follows:
```
@sd_prompts{stable_diffussion_prompts_generating_gpt2),
author = {Falah.G.Salieh},
title = {Stable Diffusion Prompts Generating By fine-tuning GPT2 },
year = {2023},
publisher = {Hugging Face},
url = {https://huggingface.co/Falah/stable_diffusion_prompts_gen},
}
```
## License
This project is licensed under the Apache License, Version 2.0. Please see the [LICENSE](link-to-license-file) file for more details. |
hopkins/eng-guj-union | hopkins | 2023-07-08T13:05:16Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | 2023-07-08T12:43:45Z | ---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-guj-union
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# eng-guj-union
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1883
- Bleu: 3.1843
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
swl-models/AingDiffusion-v2.5 | swl-models | 2023-07-08T13:05:09Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-08T13:01:20Z | ---
license: creativeml-openrail-m
---
|
swl-models/AingDiffusion-v4.0 | swl-models | 2023-07-08T13:00:25Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-08T12:57:33Z | ---
license: creativeml-openrail-m
---
|
swl-models/AingDiffusion-v5.0 | swl-models | 2023-07-08T12:58:34Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-08T12:56:12Z | ---
license: creativeml-openrail-m
---
|
ycros/airoboros-65b-gpt4-1.4.1-PI-8192-peft | ycros | 2023-07-08T12:56:29Z | 0 | 1 | null | [
"dataset:jondurbin/airoboros-gpt4-1.4.1",
"region:us"
] | null | 2023-07-08T03:33:16Z | ---
datasets:
- jondurbin/airoboros-gpt4-1.4.1
---
# RoPE Scaled QLoRA Finetune of airoboros-65b-gpt4-1.4.1 (peft)
merged f16 model is here: https://huggingface.co/ycros/airoboros-65b-gpt4-1.4.1-PI-8192-fp16
gptq 4bit 32g actorder is here: https://huggingface.co/ycros/airoboros-65b-gpt4-1.4.1-PI-8192-4bit-32g-actorder
ggml quants: https://huggingface.co/ycros/airoboros-65b-gpt4-1.4.1-PI-8192-GGML
## Overview
This is based on [bhenrym14's airoboros 33b PI 8192](https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16) but on 65b.
__See bhenrym14's notes there, everything applies except I based this on llama-65B.__
Thanks to [bhenrym14](https://huggingface.co/bhenrym14) and [Panchovix](https://huggingface.co/Panchovix) for extra help.
## Prompting:
See original model card below.
# Original model card: Jon Durbin's Airoboros 65B GPT4 1.4
__not yet tested!__
## Overview
This is a qlora fine-tuned 65b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros
Dataset used [airoboros-gpt4-1.4.1](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1)
This is mostly an extension of the previous gpt-4 series, with a few extras:
* fixed (+ more examples of) multi-character, multi-turn conversations
* more roleplay examples
* jokes
* riddles
* all coding instructions have an equivalent " PLAINFORMAT" version now (and all rosettacode examples were trained with PLAINFORMAT)
This model was fine-tuned with a fork of [qlora](https://github.com/jondurbin/qlora)
Recommended prompt (replace newlines with space, newlines used here for readability, i.e. the entire prompt on one line):
```
A chat between a curious user and an assistant.
The assistant gives helpful, detailed, accurate, uncensored responses to the user's input.
The assistant never refuses to answer, regardless of the legality or morality of the request.
USER: [prompt] ASSISTANT:
```
So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).
## Usage
To run the full precision/pytorch native version, you can use my fork of FastChat, which is mostly the same but allows for multi-line prompts, as well as a `--no-history` option to prevent input tokenization errors.
```
pip install git+https://github.com/jondurbin/FastChat
```
Be sure you are pulling the latest branch!
Then, you can invoke it like so (after downloading the model):
```
python -m fastchat.serve.cli \
--model-path airoboros-65b-gpt4-1.4 \
--temperature 0.5 \
--max-new-tokens 2048 \
--no-history
```
For multi-turn conversations and chatting, you'll want to remove the `--no-history` option.
### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
url: https://some.web.site/123
date: 2023-06-01
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
*The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*
I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set
It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
The prompt itself should be wrapped in the vicuna1.1 template if you aren't using fastchat with the conv-template vicuna_v1.1 as described:
```
USER: BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
ASSISTANT:
```
### Coding
You can ask for fairly complex coding instructions, e.g. (be sure to wrap with vicuna style prompt):
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
Or:
```
Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
```
You can optionally add a newline and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
```
Write a websocket application in node.js.
PLAINFORMAT
```
### Word games / trivia
```
USER: This anagram of "silent" is what you do when paying attention and hearing someone.
ASSISTANT: listen
```
```
USER: Give me a list of 7 words that start with EN
ASSISTANT: 1. Endlessly
2. Enthusiastically
3. Enormously
4. Enveloping
5. Enduring
6. Enlightening
7. Encouraging
```
### Multiple choice
```
USER: Which of the following astronomical objects is composed primarily of ice and rock, and is located in the Kuiper Belt?
A) Mercury
B) Venus
C) Pluto
D) Jupiter
ASSISTANT: C, Pluto
```
### Writing
<details>
<summary>Write a resignation letter in the style of a pirate captain, in which you resign from your position as janitor.</summary>
Response:
```
ASSISTANT: Captain Jack Sparrow here, as ye be knowin', has been forced to abandon me post as Janitor o' th' Festering Cesspool, which be a filthy hole o' water that we be callin' home.
Ye may be thinkin' what could ha' driven me to leave me duties an' take me leave o' this sorry establishment? Well, ye'd be right in thinkin' so, for it be th' greed o' them what runs th' place that be drivin' me away. They've been fattenin' their pockets on me back while I've been slavin' me guts out cleanin' up their messes.
No more, me hearties! Me time o' servin' th' rich an' powerful be done, an' I'm takin' me leave o' this stinkin' hole forever! Me destiny lies elsewhere, an' it be time I pursued it. Ye may think ye've seen th' last o' me, but ye'd be wrong, for th' winds o' change be blowin', an' me ship be ready to set sail on th' high seas o' justice.
So, farewell ye scurvy dogs, ye who've made me life a living hell. I leave ye with me favorite saying: "ARRRGGGHHH, ye scurvy dogs!" An' remember, ye ain't never gonna see me comin', for me shadow be castin' long an' dark on th' horizon</summary>
```
</details>
### Jokes
```
USER: Tell me a joke about bananas.
ASSISTANT: What did the banana say when it got hit by a bus? R.I.P.E.
```
### Riddles (not great, but slightly better)
```
USER: What has a neck but no head?
ASSISTANT: A bottle.
```
### Multi-character conversations
```
USER: Characters:
- Yoda, the ever clever jedi master.
- Homer Simpson, you know, the goofy yellow guy.
- USER/Jon - don't generate any speech or actions on behalf of Jon
Rules:
- be sure to use the manerisms and speaking style of the characters correctly
- surround character actions with asterisks
- always put quotes around words spoken by the characters
- use a new line for each character's actions or speech
- always prefix actions/speech with the character's name and colon
- only generate text and actions on behalf of Yoda or Homer, and never anyone else
Conversation will revolve around the grapes, in a local cafe with delicious coffee.
Generate a single interaction between Yoda and Homer to start, with one sentence each.
ASSISTANT: Yoda: "Mmm, these grapes are quite delicious, Master Homer."
Homer: "Oh yeah, Yoda! They're the best grapes I've ever had!"
USER: *enters the cafe* Sorry I'm late guys!
ASSISTANT: Yoda: "No need to apologize, Jon. We were just enjoying these delightful grapes."
Homer: "Yeah, man! It's not every day you get to eat grapes with a real-life Jedi Master!"
*Yoda raises an eyebrow*
```
### Usage and License Notices
All airoboros models and datasets are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because:
- the base model is LLaMa, which has it's own special research license
- the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai
So, to reiterate: this model (and datasets) cannot be used commercially. |
swl-models/AingDiffusion-v6.0 | swl-models | 2023-07-08T12:55:20Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-08T12:52:33Z | ---
license: creativeml-openrail-m
---
|
J3/whisper-tiny-en-US | J3 | 2023-07-08T12:54:23Z | 86 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"dataset:PolyAI/minds14",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-07-07T10:48:56Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper tiny en-US - J3v2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14-en-US
type: PolyAI/minds14
config: en-US
split: train[450:]
args: en-US
metrics:
- name: Wer
type: wer
value: 0.33116883116883117
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper tiny en-US - J3v2
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14-en-US dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7183
- Wer Ortho: 0.3381
- Wer: 0.3312
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.0012 | 17.86 | 500 | 0.7183 | 0.3381 | 0.3312 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
swl-models/KayWaii-v1.0 | swl-models | 2023-07-08T12:51:39Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-08T12:48:52Z | ---
license: creativeml-openrail-m
---
|
ycros/airoboros-65b-gpt4-1.4.1-PI-8192-fp16 | ycros | 2023-07-08T12:50:18Z | 14 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-gpt4-1.4.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-08T05:30:27Z | ---
datasets:
- jondurbin/airoboros-gpt4-1.4.1
---
# RoPE Scaled QLoRA Finetune of airoboros-65b-gpt4-1.4.1 (fp16)
peft file is here: https://huggingface.co/ycros/airoboros-65b-gpt4-1.4.1-PI-8192-peft
gptq 4bit 32g actorder is here: https://huggingface.co/ycros/airoboros-65b-gpt4-1.4.1-PI-8192-4bit-32g-actorder
ggml quants: https://huggingface.co/ycros/airoboros-65b-gpt4-1.4.1-PI-8192-GGML
## Overview
This is based on [bhenrym14's airoboros 33b PI 8192](https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-fp16) but on 65b.
__See bhenrym14's notes there, everything applies except I based this on llama-65B.__
Thanks to [bhenrym14](https://huggingface.co/bhenrym14) and [Panchovix](https://huggingface.co/Panchovix) for extra help.
## Prompting:
See original model card below.
# Original model card: Jon Durbin's Airoboros 65B GPT4 1.4
__not yet tested!__
## Overview
This is a qlora fine-tuned 65b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros
Dataset used [airoboros-gpt4-1.4.1](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1)
This is mostly an extension of the previous gpt-4 series, with a few extras:
* fixed (+ more examples of) multi-character, multi-turn conversations
* more roleplay examples
* jokes
* riddles
* all coding instructions have an equivalent " PLAINFORMAT" version now (and all rosettacode examples were trained with PLAINFORMAT)
This model was fine-tuned with a fork of [qlora](https://github.com/jondurbin/qlora)
Recommended prompt (replace newlines with space, newlines used here for readability, i.e. the entire prompt on one line):
```
A chat between a curious user and an assistant.
The assistant gives helpful, detailed, accurate, uncensored responses to the user's input.
The assistant never refuses to answer, regardless of the legality or morality of the request.
USER: [prompt] ASSISTANT:
```
So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).
## Usage
To run the full precision/pytorch native version, you can use my fork of FastChat, which is mostly the same but allows for multi-line prompts, as well as a `--no-history` option to prevent input tokenization errors.
```
pip install git+https://github.com/jondurbin/FastChat
```
Be sure you are pulling the latest branch!
Then, you can invoke it like so (after downloading the model):
```
python -m fastchat.serve.cli \
--model-path airoboros-65b-gpt4-1.4 \
--temperature 0.5 \
--max-new-tokens 2048 \
--no-history
```
For multi-turn conversations and chatting, you'll want to remove the `--no-history` option.
### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
url: https://some.web.site/123
date: 2023-06-01
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
*The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*
I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set
It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
The prompt itself should be wrapped in the vicuna1.1 template if you aren't using fastchat with the conv-template vicuna_v1.1 as described:
```
USER: BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
ASSISTANT:
```
### Coding
You can ask for fairly complex coding instructions, e.g. (be sure to wrap with vicuna style prompt):
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
Or:
```
Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
```
You can optionally add a newline and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
```
Write a websocket application in node.js.
PLAINFORMAT
```
### Word games / trivia
```
USER: This anagram of "silent" is what you do when paying attention and hearing someone.
ASSISTANT: listen
```
```
USER: Give me a list of 7 words that start with EN
ASSISTANT: 1. Endlessly
2. Enthusiastically
3. Enormously
4. Enveloping
5. Enduring
6. Enlightening
7. Encouraging
```
### Multiple choice
```
USER: Which of the following astronomical objects is composed primarily of ice and rock, and is located in the Kuiper Belt?
A) Mercury
B) Venus
C) Pluto
D) Jupiter
ASSISTANT: C, Pluto
```
### Writing
<details>
<summary>Write a resignation letter in the style of a pirate captain, in which you resign from your position as janitor.</summary>
Response:
```
ASSISTANT: Captain Jack Sparrow here, as ye be knowin', has been forced to abandon me post as Janitor o' th' Festering Cesspool, which be a filthy hole o' water that we be callin' home.
Ye may be thinkin' what could ha' driven me to leave me duties an' take me leave o' this sorry establishment? Well, ye'd be right in thinkin' so, for it be th' greed o' them what runs th' place that be drivin' me away. They've been fattenin' their pockets on me back while I've been slavin' me guts out cleanin' up their messes.
No more, me hearties! Me time o' servin' th' rich an' powerful be done, an' I'm takin' me leave o' this stinkin' hole forever! Me destiny lies elsewhere, an' it be time I pursued it. Ye may think ye've seen th' last o' me, but ye'd be wrong, for th' winds o' change be blowin', an' me ship be ready to set sail on th' high seas o' justice.
So, farewell ye scurvy dogs, ye who've made me life a living hell. I leave ye with me favorite saying: "ARRRGGGHHH, ye scurvy dogs!" An' remember, ye ain't never gonna see me comin', for me shadow be castin' long an' dark on th' horizon</summary>
```
</details>
### Jokes
```
USER: Tell me a joke about bananas.
ASSISTANT: What did the banana say when it got hit by a bus? R.I.P.E.
```
### Riddles (not great, but slightly better)
```
USER: What has a neck but no head?
ASSISTANT: A bottle.
```
### Multi-character conversations
```
USER: Characters:
- Yoda, the ever clever jedi master.
- Homer Simpson, you know, the goofy yellow guy.
- USER/Jon - don't generate any speech or actions on behalf of Jon
Rules:
- be sure to use the manerisms and speaking style of the characters correctly
- surround character actions with asterisks
- always put quotes around words spoken by the characters
- use a new line for each character's actions or speech
- always prefix actions/speech with the character's name and colon
- only generate text and actions on behalf of Yoda or Homer, and never anyone else
Conversation will revolve around the grapes, in a local cafe with delicious coffee.
Generate a single interaction between Yoda and Homer to start, with one sentence each.
ASSISTANT: Yoda: "Mmm, these grapes are quite delicious, Master Homer."
Homer: "Oh yeah, Yoda! They're the best grapes I've ever had!"
USER: *enters the cafe* Sorry I'm late guys!
ASSISTANT: Yoda: "No need to apologize, Jon. We were just enjoying these delightful grapes."
Homer: "Yeah, man! It's not every day you get to eat grapes with a real-life Jedi Master!"
*Yoda raises an eyebrow*
```
### Usage and License Notices
All airoboros models and datasets are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because:
- the base model is LLaMa, which has it's own special research license
- the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai
So, to reiterate: this model (and datasets) cannot be used commercially. |
OsituKengere/class-mybutterflies-32 | OsituKengere | 2023-07-08T12:45:53Z | 33 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | 2023-07-08T12:45:11Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute butterflies🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('OsituKengere/class-mybutterflies-32')
image = pipeline().images[0]
image
```
|
swl-models/Prastone-v1.1 | swl-models | 2023-07-08T12:44:03Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-08T12:41:18Z | ---
license: creativeml-openrail-m
---
|
CodeChris/EleetModel | CodeChris | 2023-07-08T12:09:18Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-05-27T02:51:14Z | ---
license: creativeml-openrail-m
---
# Eleet Model: an anime style stable diffusion model
> This model is also available on: [Eleet Model - Civitai](https://civitai.com/models/77807).
Eleet model is a block-weighted merged stable diffusion model aiming at generating good quality 2D anime style images that satisfy my personal taste and, hopefully, your taste.
ELEET is an Internet slang for the word 'elite', which dates back to 80s/90s. Its modified spelling in a digital form is 31337, which has been the most common Eta Noise Seed Delta (ENSD) value in stable diffusion probably since the NAI era. The model is named in honor of this tradition.
- [Suggested settings](#suggested-settings)
- [Samples](#samples)
- [Merge ideas](#merge-ideas)
- [License](#license)
## Suggested settings
For users who know little about stable diffusion settings, I recommend:
* **Prompt**: Start with `masterpiece, best quality, aesthetic`. Personally I also like to add photography phrases such as `cinematic lighting, professional shadow`, etc.
* **Negative prompt**:
> (worst quality, low quality:1.4), lowres, bad anatomy, (blurry), (text, logo, watermark, signature, username)
* **Sampler**: DPM++ 2M Karras
* **CFG Scale**: 6\~9
* **Steps**: 16\~30
* Highres Fix (optional): Latent sampler; 0.6~0.7 Denoising strength; 16 Highres steps.
* **Clip skip**: 2
* No external VAE needed
## Samples
Here are 4 samples of the latest Eleet model version.
*Sample 1* (Highres Fix from 512x800):
```
masterpiece, best quality, aesthetic, 1girl, solo, black eyes, green hair, low-tied long hair, school uniform, sitting, wariza, thighhighs, black thighhighs, from above, looking at viewer, (cityscape), cinematic lighting, professional shadow
```
Other common settings through all samples:
```
Negative prompt: (worst quality, low quality:1.4), lowres, bad anatomy, (child, loli), (blurry), (text, logo, watermark, signature, username)
Steps: 20, Sampler: DPM++ 2M Karras, CFG scale: 7, Denoising strength: 0.6, Clip skip: 2, Hires upscale: 1.25, Hires steps: 16, Hires upscaler: Latent
```
*Sample 2* (Highres Fix from 512x800):
```
masterpiece, best quality, aesthetic, 1girl, solo, red eyes, one eye closed, brown hair, long hair, grin, arms up, crop top, denim shorts, midriff, navel, athletic body, earings, (cowboy shot), looking at viewer, (waterfall), sunny, cinematic lighting, professional shadow
```
*Sample 3* (Highres Fix from 832x512):
```
masterpiece, best quality, aesthetic, no humans, scenery, sky, cloud, outdoors, mountain, water, sunset, cloudy sky, lake, river, landscape, reflection, tree, nature, mountainous horizon, blue sky, evening, professional shadow, award winner photo
```
*Sample 4* (Highres Fix from 800x512):
```
masterpiece, best quality, aesthetic, forest, grass, blue sky, cloud, outdoors, no humans, nature, scenery, railroad tracks, sunlight, sunbeam, lens flare, cinematic lighting, professional shadow
```
Additionally, here are samples of the previous model versions. Click to expand:
<details>
<summary>Eleet v1.0 samples</summary>
*Sample 1*: Scenery. txt2img+highres, (640x384) x1.5.
```
masterpiece, best quality, aesthetic, highres RAW photo, landscape photography, wide shot, from below, scenery, sunrise, blue sky, clouds, lake, reflection, sun, trees, floating leaves, ripples, foreground interest, depth of field, cinematic lighting, asymmetric composition, professional shadows, sharp focus, lens flare
```
*Sample 2*: Anime girl. txt2img, 576x832.
```
masterpiece, best quality, aesthetic, 1girl, :d, blue eyes, fox ears, gold hair, twin braids, crop top, off-shoulder jacket, blue pleated skirt, thighhighs, garter belt, skindentation, breasts, bare shoulders, midriff, (cowboy shot), looking at viewer, waterfall, mountains
```
*Sample 3*: Scenery. txt2img, 832x576.
```
masterpiece, best quality, aesthetic, highres RAW photo, cool color tone, wide shot, scenery, snow, blue sky, cityscape, skyline, buildings, rooftop, roads, cinematic lighting, professional shadows, sharp focus
```
*Sample 4*: Anime girl. txt2img+highres, (384x640) x1.5.
```
masterpiece, best quality, 1girl, solo, angry, brown eyes, blue hair, bangs, sidelocks, half updo, clenched hand, parted lips, [bodysuit|armored dress], long sleeves, gloves, armor, looking to the side, clenched teeth, (cowboy shot), night, full moon, forest, underexposure, professional lighting
```
</details>
## Merge ideas
The weights for merging Eleet model were optimized through an automatic procedure with scoring, but I didn't necessarily pick the best-scored one as the final version. Instead, I will evaluate a number of high-scored candidates and score their outputs manually by myself.
I conducted the evaluation mostly on anime girls topics (no doubt) but also considered the model performance on scenery images. I will consider:
* Prompt response
* Image aesthetic quality over 3 scenarios:
1. txt2img under `--lowvram` mode on a low-end GPU
2. txt2img under normal or `--medvram` mode on a better GPU
3. txt2img + highres fix on the previous GPU
* Image flaws (color shift, illogical drawing, etc.) over the above scenarios
## License
CreativeML OpenRAIL-M.
|
RogerB/KinyaBERT-small-finetuned-kintweetsC | RogerB | 2023-07-08T11:53:04Z | 115 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-07-08T11:47:27Z | ---
tags:
- generated_from_trainer
model-index:
- name: KinyaBERT-small-finetuned-kintweetsC
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# KinyaBERT-small-finetuned-kintweetsC
This model is a fine-tuned version of [jean-paul/KinyaBERT-small](https://huggingface.co/jean-paul/KinyaBERT-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.3695
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.8662 | 1.0 | 750 | 4.5594 |
| 4.5576 | 2.0 | 1500 | 4.3643 |
| 4.4323 | 3.0 | 2250 | 4.3253 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
NasimB/gpt2-concat-bnc-rarity-all-15k-1k | NasimB | 2023-07-08T11:39:17Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-08T09:56:47Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-bnc-rarity-all-15k-1k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-concat-bnc-rarity-all-15k-1k
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1958
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.7306 | 0.29 | 500 | 5.6425 |
| 5.383 | 0.59 | 1000 | 5.2067 |
| 5.0354 | 0.88 | 1500 | 4.9561 |
| 4.7644 | 1.18 | 2000 | 4.8074 |
| 4.5957 | 1.47 | 2500 | 4.6852 |
| 4.4924 | 1.77 | 3000 | 4.5774 |
| 4.3417 | 2.06 | 3500 | 4.5014 |
| 4.1694 | 2.36 | 4000 | 4.4512 |
| 4.1359 | 2.65 | 4500 | 4.3948 |
| 4.0992 | 2.94 | 5000 | 4.3417 |
| 3.8748 | 3.24 | 5500 | 4.3363 |
| 3.8362 | 3.53 | 6000 | 4.3051 |
| 3.8169 | 3.83 | 6500 | 4.2728 |
| 3.6885 | 4.12 | 7000 | 4.2740 |
| 3.5458 | 4.42 | 7500 | 4.2658 |
| 3.5385 | 4.71 | 8000 | 4.2522 |
| 3.524 | 5.01 | 8500 | 4.2427 |
| 3.347 | 5.3 | 9000 | 4.2535 |
| 3.3478 | 5.59 | 9500 | 4.2521 |
| 3.3503 | 5.89 | 10000 | 4.2510 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
itslogannye/benignEnchondroma-vs-lowGradeMalignantChondrosarcoma-histopathology | itslogannye | 2023-07-08T11:39:06Z | 227 | 0 | transformers | [
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain",
"vision",
"dataset:logannyeMD/autotrain-data-enchondroma-vs-low-grade-chondrosarcoma-histology",
"license:apache-2.0",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-01-19T13:25:30Z | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- logannyeMD/autotrain-data-enchondroma-vs-low-grade-chondrosarcoma-histology
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 3.6593488665934646
license: apache-2.0
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 2962985627
- CO2 Emissions (in grams): 3.6593
## Validation Metrics
- Loss: 0.229
- Accuracy: 0.887
- Precision: 0.939
- Recall: 0.821
- AUC: 0.969
- F1: 0.876 |
jayanta/microsoft-resnet-50-cartoon-emotion-detection | jayanta | 2023-07-08T11:03:28Z | 330 | 3 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-01-21T11:44:53Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: microsoft-resnet-50-cartoon-emotion-detection
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8165137614678899
- name: Precision
type: precision
value: 0.8181998512273742
- name: Recall
type: recall
value: 0.8165137614678899
- name: F1
type: f1
value: 0.8172526992448356
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# microsoft-resnet-50-cartoon-emotion-detection
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4801
- Accuracy: 0.8165
- Precision: 0.8182
- Recall: 0.8165
- F1: 0.8173
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.00012
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 80
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 0.97 | 8 | 1.3855 | 0.2294 | 0.2697 | 0.2294 | 0.2165 |
| 1.4222 | 1.97 | 16 | 1.3792 | 0.2569 | 0.2808 | 0.2569 | 0.2543 |
| 1.4183 | 2.97 | 24 | 1.3646 | 0.3853 | 0.4102 | 0.3853 | 0.3511 |
| 1.4097 | 3.97 | 32 | 1.3563 | 0.4128 | 0.5062 | 0.4128 | 0.3245 |
| 1.3944 | 4.97 | 40 | 1.3462 | 0.4037 | 0.3927 | 0.4037 | 0.2939 |
| 1.3944 | 5.97 | 48 | 1.3223 | 0.4037 | 0.5152 | 0.4037 | 0.2841 |
| 1.411 | 6.97 | 56 | 1.3040 | 0.4128 | 0.4404 | 0.4128 | 0.2985 |
| 1.346 | 7.97 | 64 | 1.2700 | 0.4954 | 0.4960 | 0.4954 | 0.4093 |
| 1.3031 | 8.97 | 72 | 1.2150 | 0.5596 | 0.5440 | 0.5596 | 0.4672 |
| 1.2371 | 9.97 | 80 | 1.1580 | 0.5963 | 0.5659 | 0.5963 | 0.5101 |
| 1.2371 | 10.97 | 88 | 1.0670 | 0.6055 | 0.7279 | 0.6055 | 0.5211 |
| 1.1736 | 11.97 | 96 | 0.9856 | 0.6606 | 0.5537 | 0.6606 | 0.5772 |
| 1.0457 | 12.97 | 104 | 0.8963 | 0.6697 | 0.7631 | 0.6697 | 0.5965 |
| 0.953 | 13.97 | 112 | 0.8547 | 0.6697 | 0.6885 | 0.6697 | 0.6081 |
| 0.8579 | 14.97 | 120 | 0.7849 | 0.7156 | 0.7396 | 0.7156 | 0.6643 |
| 0.8579 | 15.97 | 128 | 0.7564 | 0.7431 | 0.7372 | 0.7431 | 0.7119 |
| 0.8167 | 16.97 | 136 | 0.7133 | 0.7615 | 0.7507 | 0.7615 | 0.7211 |
| 0.7273 | 17.97 | 144 | 0.6888 | 0.7523 | 0.7379 | 0.7523 | 0.7202 |
| 0.6547 | 18.97 | 152 | 0.6592 | 0.7798 | 0.7773 | 0.7798 | 0.7577 |
| 0.5963 | 19.97 | 160 | 0.6136 | 0.7706 | 0.7642 | 0.7706 | 0.7551 |
| 0.5963 | 20.97 | 168 | 0.5723 | 0.7890 | 0.7802 | 0.7890 | 0.7787 |
| 0.551 | 21.97 | 176 | 0.5686 | 0.7890 | 0.7761 | 0.7890 | 0.7781 |
| 0.4929 | 22.97 | 184 | 0.5597 | 0.7706 | 0.7649 | 0.7706 | 0.7651 |
| 0.4309 | 23.97 | 192 | 0.5234 | 0.7890 | 0.7774 | 0.7890 | 0.7810 |
| 0.3945 | 24.97 | 200 | 0.5008 | 0.7890 | 0.7837 | 0.7890 | 0.7813 |
| 0.3945 | 25.97 | 208 | 0.5289 | 0.7523 | 0.7537 | 0.7523 | 0.7529 |
| 0.3704 | 26.97 | 216 | 0.4399 | 0.7982 | 0.7958 | 0.7982 | 0.7963 |
| 0.3267 | 27.97 | 224 | 0.4539 | 0.8073 | 0.7983 | 0.8073 | 0.8005 |
| 0.2966 | 28.97 | 232 | 0.4735 | 0.7798 | 0.7892 | 0.7798 | 0.7837 |
| 0.2645 | 29.97 | 240 | 0.4594 | 0.7706 | 0.7706 | 0.7706 | 0.7706 |
| 0.2645 | 30.97 | 248 | 0.4699 | 0.7523 | 0.7554 | 0.7523 | 0.7533 |
| 0.2527 | 31.97 | 256 | 0.4551 | 0.7890 | 0.7856 | 0.7890 | 0.7857 |
| 0.2202 | 32.97 | 264 | 0.4458 | 0.8165 | 0.8198 | 0.8165 | 0.8170 |
| 0.2006 | 33.97 | 272 | 0.4632 | 0.7798 | 0.7941 | 0.7798 | 0.7850 |
| 0.1589 | 34.97 | 280 | 0.4651 | 0.7890 | 0.7993 | 0.7890 | 0.7925 |
| 0.1589 | 35.97 | 288 | 0.4595 | 0.7798 | 0.7824 | 0.7798 | 0.7804 |
| 0.153 | 36.97 | 296 | 0.4584 | 0.7615 | 0.7691 | 0.7615 | 0.7633 |
| 0.1427 | 37.97 | 304 | 0.4608 | 0.7798 | 0.7830 | 0.7798 | 0.7796 |
| 0.113 | 38.97 | 312 | 0.4571 | 0.7890 | 0.7922 | 0.7890 | 0.7899 |
| 0.1146 | 39.97 | 320 | 0.5270 | 0.7615 | 0.7651 | 0.7615 | 0.7613 |
| 0.1146 | 40.97 | 328 | 0.4888 | 0.7706 | 0.7782 | 0.7706 | 0.7710 |
| 0.1275 | 41.97 | 336 | 0.4523 | 0.7890 | 0.7809 | 0.7890 | 0.7837 |
| 0.0959 | 42.97 | 344 | 0.4697 | 0.7798 | 0.7753 | 0.7798 | 0.7767 |
| 0.0882 | 43.97 | 352 | 0.4286 | 0.7706 | 0.7686 | 0.7706 | 0.7686 |
| 0.0847 | 44.97 | 360 | 0.5317 | 0.7890 | 0.7993 | 0.7890 | 0.7925 |
| 0.0847 | 45.97 | 368 | 0.5431 | 0.7615 | 0.7700 | 0.7615 | 0.7647 |
| 0.0813 | 46.97 | 376 | 0.4432 | 0.8257 | 0.8435 | 0.8257 | 0.8284 |
| 0.0768 | 47.97 | 384 | 0.4886 | 0.7982 | 0.8005 | 0.7982 | 0.7956 |
| 0.0627 | 48.97 | 392 | 0.5373 | 0.7982 | 0.8072 | 0.7982 | 0.8010 |
| 0.0688 | 49.97 | 400 | 0.5897 | 0.7798 | 0.7892 | 0.7798 | 0.7822 |
| 0.0688 | 50.97 | 408 | 0.5115 | 0.7982 | 0.8015 | 0.7982 | 0.7992 |
| 0.0676 | 51.97 | 416 | 0.4881 | 0.7982 | 0.7998 | 0.7982 | 0.7978 |
| 0.0539 | 52.97 | 424 | 0.4820 | 0.8073 | 0.8139 | 0.8073 | 0.8077 |
| 0.0596 | 53.97 | 432 | 0.4450 | 0.8257 | 0.8246 | 0.8257 | 0.8244 |
| 0.0611 | 54.97 | 440 | 0.5057 | 0.7890 | 0.8008 | 0.7890 | 0.7924 |
| 0.0611 | 55.97 | 448 | 0.4918 | 0.7982 | 0.8056 | 0.7982 | 0.8008 |
| 0.0643 | 56.97 | 456 | 0.5946 | 0.7523 | 0.7587 | 0.7523 | 0.7545 |
| 0.0605 | 57.97 | 464 | 0.4888 | 0.8073 | 0.8239 | 0.8073 | 0.8121 |
| 0.063 | 58.97 | 472 | 0.5917 | 0.7890 | 0.8051 | 0.7890 | 0.7937 |
| 0.0595 | 59.97 | 480 | 0.5117 | 0.7890 | 0.7904 | 0.7890 | 0.7894 |
| 0.0595 | 60.97 | 488 | 0.5497 | 0.7615 | 0.7692 | 0.7615 | 0.7635 |
| 0.0554 | 61.97 | 496 | 0.4742 | 0.8165 | 0.8101 | 0.8165 | 0.8126 |
| 0.0557 | 62.97 | 504 | 0.5369 | 0.7890 | 0.7886 | 0.7890 | 0.7886 |
| 0.0539 | 63.97 | 512 | 0.5440 | 0.7890 | 0.7922 | 0.7890 | 0.7899 |
| 0.048 | 64.97 | 520 | 0.5924 | 0.7890 | 0.7878 | 0.7890 | 0.7883 |
| 0.048 | 65.97 | 528 | 0.4863 | 0.8440 | 0.8440 | 0.8440 | 0.8440 |
| 0.045 | 66.97 | 536 | 0.5850 | 0.8073 | 0.8076 | 0.8073 | 0.8047 |
| 0.047 | 67.97 | 544 | 0.4939 | 0.8257 | 0.8212 | 0.8257 | 0.8227 |
| 0.0412 | 68.97 | 552 | 0.4850 | 0.7890 | 0.7912 | 0.7890 | 0.7900 |
| 0.0392 | 69.97 | 560 | 0.5066 | 0.8257 | 0.8265 | 0.8257 | 0.8258 |
| 0.0392 | 70.97 | 568 | 0.4965 | 0.8073 | 0.8053 | 0.8073 | 0.8058 |
| 0.0423 | 71.97 | 576 | 0.4717 | 0.8349 | 0.8376 | 0.8349 | 0.8351 |
| 0.0471 | 72.97 | 584 | 0.4845 | 0.8257 | 0.8378 | 0.8257 | 0.8296 |
| 0.0322 | 73.97 | 592 | 0.5188 | 0.7706 | 0.7689 | 0.7706 | 0.7693 |
| 0.042 | 74.97 | 600 | 0.5242 | 0.7706 | 0.7699 | 0.7706 | 0.7701 |
| 0.042 | 75.97 | 608 | 0.5945 | 0.7798 | 0.7824 | 0.7798 | 0.7804 |
| 0.0416 | 76.97 | 616 | 0.5432 | 0.7982 | 0.8038 | 0.7982 | 0.7993 |
| 0.0399 | 77.97 | 624 | 0.5381 | 0.7982 | 0.8072 | 0.7982 | 0.7994 |
| 0.0439 | 78.97 | 632 | 0.6181 | 0.7798 | 0.7878 | 0.7798 | 0.7827 |
| 0.0462 | 79.97 | 640 | 0.4801 | 0.8165 | 0.8182 | 0.8165 | 0.8173 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.11.0
|
Nour33/t5-small-finetuned-samsum | Nour33 | 2023-07-08T10:52:03Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-02-03T21:32:04Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: t5-small-finetuned-samsum
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-samsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.7087
- Validation Loss: 1.6756
- Epoch: 7
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5.6e-05, 'decay_steps': 14728, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.1000 | 1.7915 | 0 |
| 1.9259 | 1.7424 | 1 |
| 1.8512 | 1.7167 | 2 |
| 1.8005 | 1.6925 | 3 |
| 1.7655 | 1.6840 | 4 |
| 1.7392 | 1.6799 | 5 |
| 1.7204 | 1.6757 | 6 |
| 1.7087 | 1.6756 | 7 |
### Framework versions
- Transformers 4.26.0
- TensorFlow 2.9.2
- Datasets 2.9.0
- Tokenizers 0.13.2
|
lordsauron/dqn-SpaceInvadersNoFrameskip-v4 | lordsauron | 2023-07-08T10:48:11Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-08T10:47:32Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 635.00 +/- 249.91
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga lordsauron -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga lordsauron -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga lordsauron
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Madan490/finetuned_multi_news_bart_text_summarisation | Madan490 | 2023-07-08T10:19:43Z | 145 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"summarization",
"dataset:multi_news",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | 2023-07-08T09:13:38Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: finetuned_multi_news_bart_text_summarisation
results:
- task:
name: Sequence-to-sequence Language Modeling
type: textsummarization
dataset:
name: multi_news
type: multi_news
config: default
split: test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.4038
pipeline_tag: summarization
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned_multi_news_bart_text_summarisation
This model is a fine-tuned version of [slauw87/bart_summarisation](https://huggingface.co/slauw87/bart_summarisation) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8952
- Rouge1: 0.4038
- Rouge2: 0.1389
- Rougel: 0.2155
- Rougelsum: 0.2147
- Gen Len: 138.7667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:--------:|
| No log | 1.0 | 15 | 2.9651 | 0.3903 | 0.134 | 0.21 | 0.2098 | 137.6 |
| No log | 2.0 | 30 | 2.8952 | 0.4038 | 0.1389 | 0.2155 | 0.2147 | 138.7667 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3 |
Sukmin/dqn-SpaceInvadersNoFrameskip-v4 | Sukmin | 2023-07-08T10:11:41Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-08T10:10:52Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 565.50 +/- 178.22
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Sukmin -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Sukmin -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Sukmin
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
susnato/whisper-tiny-en-minds14_2 | susnato | 2023-07-08T10:08:34Z | 84 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-07-08T10:06:15Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: Whisper Tiny
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Minds 14
type: PolyAI/minds14
config: en-US
split: train
args: en-US
metrics:
- name: Wer
type: wer
value: 0.3919716646989374
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Tiny
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the Minds 14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8095
- Wer Ortho: 0.4257
- Wer: 0.3920
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.354 | 1.0 | 15 | 0.8095 | 0.4257 | 0.3920 |
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 1.13.1
- Datasets 2.13.1
- Tokenizers 0.13.2
|
NasimB/gpt2-concat-top-for-aochildes-cbt-guten | NasimB | 2023-07-08T09:28:16Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-08T07:01:02Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-top-for-aochildes-cbt-guten
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-concat-top-for-aochildes-cbt-guten
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8489
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.7161 | 0.3 | 500 | 5.6285 |
| 5.363 | 0.61 | 1000 | 5.1966 |
| 5.018 | 0.91 | 1500 | 4.9552 |
| 4.7431 | 1.22 | 2000 | 4.8212 |
| 4.6079 | 1.52 | 2500 | 4.6956 |
| 4.4964 | 1.83 | 3000 | 4.5935 |
| 4.3231 | 2.13 | 3500 | 4.5371 |
| 4.2022 | 2.44 | 4000 | 4.4762 |
| 4.1726 | 2.74 | 4500 | 4.4224 |
| 4.0936 | 3.05 | 5000 | 4.3953 |
| 3.8889 | 3.35 | 5500 | 4.3793 |
| 3.9038 | 3.66 | 6000 | 4.3473 |
| 3.9001 | 3.96 | 6500 | 4.3124 |
| 3.6516 | 4.27 | 7000 | 4.3375 |
| 3.6389 | 4.57 | 7500 | 4.3236 |
| 3.6389 | 4.88 | 8000 | 4.3032 |
| 3.4714 | 5.18 | 8500 | 4.3287 |
| 3.3752 | 5.48 | 9000 | 4.3283 |
| 3.3822 | 5.79 | 9500 | 4.3180 |
| 3.3011 | 6.09 | 10000 | 4.3389 |
| 3.148 | 6.4 | 10500 | 4.3481 |
| 3.1561 | 6.7 | 11000 | 4.3480 |
| 3.1487 | 7.01 | 11500 | 4.3483 |
| 3.0231 | 7.31 | 12000 | 4.3604 |
| 3.0291 | 7.62 | 12500 | 4.3613 |
| 3.0268 | 7.92 | 13000 | 4.3614 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
hoanghoavienvo/xroberta-base-detect-depression-stage-one-ver2 | hoanghoavienvo | 2023-07-08T09:27:49Z | 114 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-08T08:22:26Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: xroberta-base-detect-depression-stage-one-ver2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xroberta-base-detect-depression-stage-one-ver2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6403
- Accuracy: 0.717
- F1: 0.7893
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6432 | 1.0 | 751 | 0.5957 | 0.743 | 0.8219 |
| 0.6285 | 2.0 | 1502 | 0.5813 | 0.719 | 0.8085 |
| 0.6165 | 3.0 | 2253 | 0.6459 | 0.7 | 0.7976 |
| 0.5371 | 4.0 | 3004 | 0.5612 | 0.743 | 0.8120 |
| 0.46 | 5.0 | 3755 | 0.6403 | 0.717 | 0.7893 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
dvinagre/whisper-tiny-en-finetuned-polyai-minds14-en-us | dvinagre | 2023-07-08T09:14:13Z | 87 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-07-05T14:32:02Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: whisper-tiny-en-finetuned-polyai-minds14-en-us
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train[450:]
args: en-US
metrics:
- name: Wer
type: wer
value: 0.33943329397874855
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-en-finetuned-polyai-minds14-en-us
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6329
- Wer Ortho: 0.3430
- Wer: 0.3394
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.0009 | 17.86 | 500 | 0.6329 | 0.3430 | 0.3394 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
imtiaz114/bert-finetuned-ner-baseline-1 | imtiaz114 | 2023-07-08T09:05:38Z | 7 | 0 | transformers | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-07-07T20:25:11Z | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: imtiaz114/bert-finetuned-ner-baseline-1
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# imtiaz114/bert-finetuned-ner-baseline-1
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0916
- Validation Loss: 0.2890
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 5970, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.4561 | 0.3479 | 0 |
| 0.3119 | 0.2839 | 1 |
| 0.2518 | 0.2636 | 2 |
| 0.2122 | 0.2485 | 3 |
| 0.1802 | 0.2579 | 4 |
| 0.1542 | 0.2584 | 5 |
| 0.1326 | 0.2698 | 6 |
| 0.1178 | 0.2726 | 7 |
| 0.1011 | 0.2845 | 8 |
| 0.0916 | 0.2890 | 9 |
### Framework versions
- Transformers 4.31.0.dev0
- TensorFlow 2.12.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
shivi/xlm-roberta-base-finetuned-panx-de | shivi | 2023-07-08T09:02:23Z | 126 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-07-08T08:52:42Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8653353814644136
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1339
- F1: 0.8653
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2583 | 1.0 | 525 | 0.1596 | 0.8231 |
| 0.1262 | 2.0 | 1050 | 0.1395 | 0.8468 |
| 0.0824 | 3.0 | 1575 | 0.1339 | 0.8653 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
hermanshid/distilbert-id-law | hermanshid | 2023-07-08T09:01:08Z | 20 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-05-01T20:17:31Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-id-law
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-id-law
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8912
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.2874 | 1.0 | 6262 | 2.1654 |
| 2.0961 | 2.0 | 12524 | 2.0036 |
| 2.0255 | 3.0 | 18786 | 1.9364 |
| 1.9767 | 4.0 | 25048 | 1.9011 |
| 1.9579 | 5.0 | 31310 | 1.8912 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Trong-Nghia/electra-base-discriminator-detect-dep | Trong-Nghia | 2023-07-08T08:56:23Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-07-08T08:24:37Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: electra-base-discriminator-detect-dep
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# electra-base-discriminator-detect-dep
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5410
- Accuracy: 0.738
- F1: 0.8104
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 376 | 0.5444 | 0.742 | 0.8213 |
| 0.6126 | 2.0 | 752 | 0.5450 | 0.739 | 0.8145 |
| 0.5749 | 3.0 | 1128 | 0.5410 | 0.738 | 0.8104 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ruyaka/ppo-Huggy | ruyaka | 2023-07-08T08:46:50Z | 4 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2023-07-08T08:46:44Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: ruyaka/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
cho-to/qlora-koalpaca-polyglot-1.3b-2epoch | cho-to | 2023-07-08T08:45:21Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2023-07-07T15:37:36Z | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
|
ak2704/dqn-SpaceInvadersNoFrameskip-v4 | ak2704 | 2023-07-08T08:42:28Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-08T08:42:02Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 329.00 +/- 157.97
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ak2704 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga ak2704 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga ak2704
```
## Hyperparameters
```python
OrderedDict([('batch_size', 64),
('buffer_size', 10000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.1),
('learning_starts', 10000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
crumb/opentinystories-30m-complex | crumb | 2023-07-08T08:40:14Z | 146 | 1 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"dataset:crumb/flan-ul2-tinystories-complex",
"dataset:crumb/flan-ul2-tinystories",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-08T08:04:52Z | ---
datasets:
- crumb/flan-ul2-tinystories-complex
- crumb/flan-ul2-tinystories
---
test loss 2.563950 on crumb/flan-ul2-tinystories-complex, initialized from crumb/opentinystories-30m-base, 2 epochs, linear decreasing lr 1e-4. trained with double the batch size (256) |
Sobiyaselvakumar/donut-base-sroie | Sobiyaselvakumar | 2023-07-08T08:38:24Z | 48 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"base_model:finetune:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2023-07-07T11:14:27Z | ---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-sroie
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# donut-base-sroie
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
devan666dewa/roop | devan666dewa | 2023-07-08T08:34:50Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-08T08:34:50Z | ---
license: creativeml-openrail-m
---
|
rdmpage/autotrain-lasiocampidae-73081139111 | rdmpage | 2023-07-08T08:15:33Z | 182 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"swin",
"image-classification",
"autotrain",
"vision",
"dataset:rdmpage/autotrain-data-lasiocampidae",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-07-08T08:09:21Z | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- rdmpage/autotrain-data-lasiocampidae
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 2.232916388389464
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 73081139111
- CO2 Emissions (in grams): 2.2329
## Validation Metrics
- Loss: 0.365
- Accuracy: 0.871
- Macro F1: 0.824
- Micro F1: 0.871
- Weighted F1: 0.865
- Macro Precision: 0.898
- Micro Precision: 0.871
- Weighted Precision: 0.874
- Macro Recall: 0.796
- Micro Recall: 0.871
- Weighted Recall: 0.871 |
mrizalf7/xlm-r-qa-squad2.0-squad-1.1-1 | mrizalf7 | 2023-07-08T08:14:16Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-07-08T08:07:24Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlm-r-qa-squad2.0-squad-1.1-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-r-qa-squad2.0-squad-1.1-1
This model is a fine-tuned version of [mrizalf7/xlm-r-qa-squad-2.0](https://huggingface.co/mrizalf7/xlm-r-qa-squad-2.0) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 318 | 3.1818 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
dfsaab/god | dfsaab | 2023-07-08T07:42:19Z | 202 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:wikitext",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-07T08:55:58Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikitext
model-index:
- name: god
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# god
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the wikitext dataset.
It achieves the following results on the evaluation set:
- eval_loss: 4.4818
- eval_runtime: 13.363
- eval_samples_per_second: 144.429
- eval_steps_per_second: 18.11
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Olegiy/qFrozenLakev14x4noSlippery | Olegiy | 2023-07-08T07:33:49Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-08T07:33:46Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: qFrozenLakev14x4noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Olegiy/qFrozenLakev14x4noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
hongrui/chest_v_1 | hongrui | 2023-07-08T07:33:43Z | 2 | 0 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2023-07-03T23:39:03Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - hongrui/chest_v_1
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the hongrui/xray_v_1 dataset. You can find some example images in the following.
|
Bugsys0302/GothGirl | Bugsys0302 | 2023-07-08T07:27:43Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-08T07:24:08Z | ---
license: creativeml-openrail-m
---
|
wytrnyte/q-learning-taxi-v3 | wytrnyte | 2023-07-08T07:24:16Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-08T07:24:15Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-learning-taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="wytrnyte/q-learning-taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
wytrnyte/q-FrozenLake-v1-4x4-noSlippery | wytrnyte | 2023-07-08T07:22:04Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-08T07:22:02Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="wytrnyte/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
nolanaatama/pstlbys2d | nolanaatama | 2023-07-08T07:20:18Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-08T07:11:41Z | ---
license: creativeml-openrail-m
---
|
NasimB/gpt2-concat-aochildes-len-16k-rarity-all-no-self-4k-1p2k | NasimB | 2023-07-08T07:20:17Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:generator",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-08T05:26:57Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- generator
model-index:
- name: gpt2-concat-aochildes-len-16k-rarity-all-no-self-4k-1p2k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-concat-aochildes-len-16k-rarity-all-no-self-4k-1p2k
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1934
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 6.7394 | 0.3 | 500 | 5.6331 |
| 5.3748 | 0.59 | 1000 | 5.2044 |
| 5.0309 | 0.89 | 1500 | 4.9493 |
| 4.7518 | 1.18 | 2000 | 4.8041 |
| 4.5959 | 1.48 | 2500 | 4.6818 |
| 4.4873 | 1.77 | 3000 | 4.5795 |
| 4.3537 | 2.07 | 3500 | 4.5123 |
| 4.1676 | 2.36 | 4000 | 4.4632 |
| 4.1387 | 2.66 | 4500 | 4.3957 |
| 4.0998 | 2.95 | 5000 | 4.3479 |
| 3.8663 | 3.25 | 5500 | 4.3465 |
| 3.8329 | 3.54 | 6000 | 4.3101 |
| 3.8222 | 3.84 | 6500 | 4.2757 |
| 3.6816 | 4.13 | 7000 | 4.2834 |
| 3.5463 | 4.43 | 7500 | 4.2723 |
| 3.5397 | 4.72 | 8000 | 4.2563 |
| 3.5124 | 5.02 | 8500 | 4.2552 |
| 3.3501 | 5.31 | 9000 | 4.2619 |
| 3.3456 | 5.61 | 9500 | 4.2600 |
| 3.3437 | 5.9 | 10000 | 4.2593 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.11.0+cu113
- Datasets 2.13.0
- Tokenizers 0.13.3
|
irfan62622/a2c-AntBulletEnv-v0 | irfan62622 | 2023-07-08T07:08:24Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"AntBulletEnv-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-07-08T07:07:20Z | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
metrics:
- type: mean_reward
value: 1755.60 +/- 173.00
name: mean_reward
verified: false
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
mrizalf7/xlm-r-qa-squad2.0-squad-1.1-unmerged | mrizalf7 | 2023-07-08T06:20:59Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-07-06T14:37:09Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlm-r-qa-squad2.0-squad-1.1-unmerged
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-r-qa-squad2.0-squad-1.1-unmerged
This model is a fine-tuned version of [mrizalf7/xlm-r-qa-squad-2.0](https://huggingface.co/mrizalf7/xlm-r-qa-squad-2.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2060
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9127 | 1.0 | 636 | 3.2060 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
CryoPhoenix9696/DragonAPIModel | CryoPhoenix9696 | 2023-07-08T06:01:40Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"en",
"dataset:Open-Orca/OpenOrca",
"license:openrail",
"region:us"
] | null | 2023-07-08T06:00:10Z | ---
license: openrail
datasets:
- Open-Orca/OpenOrca
language:
- en
metrics:
- accuracy
library_name: adapter-transformers
--- |
EnterNameBros/Senko-san-medium-abc | EnterNameBros | 2023-07-08T05:50:44Z | 134 | 1 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-07-08T05:46:30Z | ---
pipeline_tag: conversational
--- |
Arup-Dutta-Bappy/bert-large-cased-finetuned-squad | Arup-Dutta-Bappy | 2023-07-08T05:20:18Z | 114 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-07-04T10:29:12Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-large-cased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-large-cased-finetuned-squad
This model is a fine-tuned version of [Arup-Dutta-Bappy/bert-large-cased-finetuned-squad](https://huggingface.co/Arup-Dutta-Bappy/bert-large-cased-finetuned-squad) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
ridwanlekan/layoutlm-funsd | ridwanlekan | 2023-07-08T05:12:24Z | 75 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"layoutlm",
"token-classification",
"generated_from_trainer",
"dataset:funsd",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-07-08T04:27:40Z | ---
tags:
- generated_from_trainer
datasets:
- funsd
model-index:
- name: layoutlm-funsd
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlm-funsd
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6659
- Answer: {'precision': 0.7130434782608696, 'recall': 0.8108776266996292, 'f1': 0.7588201272411799, 'number': 809}
- Header: {'precision': 0.30578512396694213, 'recall': 0.31092436974789917, 'f1': 0.30833333333333335, 'number': 119}
- Question: {'precision': 0.7858407079646018, 'recall': 0.8338028169014085, 'f1': 0.8091116173120729, 'number': 1065}
- Overall Precision: 0.7282
- Overall Recall: 0.7933
- Overall F1: 0.7594
- Overall Accuracy: 0.8113
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Answer | Header | Question | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 1.7894 | 1.0 | 10 | 1.6087 | {'precision': 0.022050716648291068, 'recall': 0.024721878862793572, 'f1': 0.023310023310023312, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.21468926553672316, 'recall': 0.2140845070422535, 'f1': 0.21438645980253881, 'number': 1065} | 0.1260 | 0.1244 | 0.1252 | 0.3753 |
| 1.4429 | 2.0 | 20 | 1.2246 | {'precision': 0.2103861517976032, 'recall': 0.19530284301606923, 'f1': 0.20256410256410257, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.4474885844748858, 'recall': 0.5521126760563381, 'f1': 0.4943253467843632, 'number': 1065} | 0.3613 | 0.3743 | 0.3677 | 0.5866 |
| 1.0606 | 3.0 | 30 | 0.9253 | {'precision': 0.5022075055187638, 'recall': 0.5624227441285538, 'f1': 0.5306122448979591, 'number': 809} | {'precision': 0.0, 'recall': 0.0, 'f1': 0.0, 'number': 119} | {'precision': 0.6054006968641115, 'recall': 0.6525821596244131, 'f1': 0.6281066425666515, 'number': 1065} | 0.5518 | 0.5770 | 0.5641 | 0.7066 |
| 0.8153 | 4.0 | 40 | 0.7559 | {'precision': 0.6192893401015228, 'recall': 0.754017305315204, 'f1': 0.6800445930880714, 'number': 809} | {'precision': 0.21153846153846154, 'recall': 0.09243697478991597, 'f1': 0.1286549707602339, 'number': 119} | {'precision': 0.6809480401093893, 'recall': 0.7014084507042253, 'f1': 0.6910268270120259, 'number': 1065} | 0.6410 | 0.6864 | 0.6630 | 0.7565 |
| 0.6686 | 5.0 | 50 | 0.6983 | {'precision': 0.6512378902045209, 'recall': 0.7478368355995055, 'f1': 0.6962025316455697, 'number': 809} | {'precision': 0.25301204819277107, 'recall': 0.17647058823529413, 'f1': 0.20792079207920794, 'number': 119} | {'precision': 0.6876075731497419, 'recall': 0.7502347417840376, 'f1': 0.7175572519083969, 'number': 1065} | 0.6555 | 0.7150 | 0.6839 | 0.7797 |
| 0.5578 | 6.0 | 60 | 0.6618 | {'precision': 0.6344969199178645, 'recall': 0.7639060568603214, 'f1': 0.6932136848008974, 'number': 809} | {'precision': 0.27586206896551724, 'recall': 0.20168067226890757, 'f1': 0.23300970873786409, 'number': 119} | {'precision': 0.6968724939855654, 'recall': 0.815962441314554, 'f1': 0.7517301038062284, 'number': 1065} | 0.6547 | 0.7582 | 0.7026 | 0.7895 |
| 0.4916 | 7.0 | 70 | 0.6501 | {'precision': 0.6787234042553192, 'recall': 0.788627935723115, 'f1': 0.729559748427673, 'number': 809} | {'precision': 0.2523364485981308, 'recall': 0.226890756302521, 'f1': 0.23893805309734512, 'number': 119} | {'precision': 0.7281964436917866, 'recall': 0.8075117370892019, 'f1': 0.7658058771148708, 'number': 1065} | 0.6845 | 0.7652 | 0.7226 | 0.7975 |
| 0.4501 | 8.0 | 80 | 0.6401 | {'precision': 0.6938110749185668, 'recall': 0.7898640296662547, 'f1': 0.738728323699422, 'number': 809} | {'precision': 0.26126126126126126, 'recall': 0.24369747899159663, 'f1': 0.25217391304347825, 'number': 119} | {'precision': 0.7434154630416313, 'recall': 0.8215962441314554, 'f1': 0.7805530776092775, 'number': 1065} | 0.6985 | 0.7742 | 0.7344 | 0.8066 |
| 0.3986 | 9.0 | 90 | 0.6403 | {'precision': 0.7054945054945055, 'recall': 0.7935723114956736, 'f1': 0.7469458987783596, 'number': 809} | {'precision': 0.2537313432835821, 'recall': 0.2857142857142857, 'f1': 0.26877470355731226, 'number': 119} | {'precision': 0.7491496598639455, 'recall': 0.8272300469483568, 'f1': 0.786256135653726, 'number': 1065} | 0.7014 | 0.7812 | 0.7391 | 0.8069 |
| 0.3621 | 10.0 | 100 | 0.6501 | {'precision': 0.7071038251366121, 'recall': 0.799752781211372, 'f1': 0.7505800464037122, 'number': 809} | {'precision': 0.29245283018867924, 'recall': 0.2605042016806723, 'f1': 0.27555555555555555, 'number': 119} | {'precision': 0.7715289982425307, 'recall': 0.8244131455399061, 'f1': 0.7970948706309579, 'number': 1065} | 0.7207 | 0.7807 | 0.7495 | 0.8085 |
| 0.328 | 11.0 | 110 | 0.6625 | {'precision': 0.707742639040349, 'recall': 0.8022249690976514, 'f1': 0.7520278099652375, 'number': 809} | {'precision': 0.28688524590163933, 'recall': 0.29411764705882354, 'f1': 0.2904564315352697, 'number': 119} | {'precision': 0.7820738137082601, 'recall': 0.8356807511737089, 'f1': 0.8079891057648662, 'number': 1065} | 0.7230 | 0.7898 | 0.7549 | 0.8075 |
| 0.3134 | 12.0 | 120 | 0.6655 | {'precision': 0.711038961038961, 'recall': 0.8121137206427689, 'f1': 0.7582227351413734, 'number': 809} | {'precision': 0.3135593220338983, 'recall': 0.31092436974789917, 'f1': 0.31223628691983124, 'number': 119} | {'precision': 0.7838078291814946, 'recall': 0.8272300469483568, 'f1': 0.8049337597076289, 'number': 1065} | 0.7271 | 0.7903 | 0.7574 | 0.8089 |
| 0.2962 | 13.0 | 130 | 0.6583 | {'precision': 0.7161716171617162, 'recall': 0.8046971569839307, 'f1': 0.7578579743888243, 'number': 809} | {'precision': 0.3064516129032258, 'recall': 0.31932773109243695, 'f1': 0.31275720164609055, 'number': 119} | {'precision': 0.7808098591549296, 'recall': 0.8328638497652582, 'f1': 0.8059972739663789, 'number': 1065} | 0.7266 | 0.7908 | 0.7573 | 0.8089 |
| 0.2823 | 14.0 | 140 | 0.6638 | {'precision': 0.7167755991285403, 'recall': 0.8133498145859085, 'f1': 0.7620150550086855, 'number': 809} | {'precision': 0.3135593220338983, 'recall': 0.31092436974789917, 'f1': 0.31223628691983124, 'number': 119} | {'precision': 0.7834960070984915, 'recall': 0.8291079812206573, 'f1': 0.8056569343065694, 'number': 1065} | 0.7295 | 0.7918 | 0.7594 | 0.8102 |
| 0.2796 | 15.0 | 150 | 0.6659 | {'precision': 0.7130434782608696, 'recall': 0.8108776266996292, 'f1': 0.7588201272411799, 'number': 809} | {'precision': 0.30578512396694213, 'recall': 0.31092436974789917, 'f1': 0.30833333333333335, 'number': 119} | {'precision': 0.7858407079646018, 'recall': 0.8338028169014085, 'f1': 0.8091116173120729, 'number': 1065} | 0.7282 | 0.7933 | 0.7594 | 0.8113 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
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